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Worked graphical examples

Three worked problems for every catalog entry: an analytical illustration, a two-condition comparison, and an interpolation prediction checked against the analytical response. Axes show physical units or stated dimensionless normalizations.

Schrödinger model · Example 1

Ground-state probability in an infinite well

Problem & parameters. A particle is confined by infinite walls at x = 0 and L. Find the normalized ground-state probability density.

L∣ψ1∣2=2sin⁡2(πx/L)L|\psi_1|^2=2\sin^2(\pi x/L)

Solution. The walls select ψ = A sin(πx/L). Normalization gives A = √(2/L); square the wavefunction to obtain the plotted density.

Schrödinger model: Ground-state probability in an infinite well. Horizontal axis: Position x / L (dimensionless). Vertical axis: Probability density × L (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.5 1.0 1.5 2.0 Probability density × L (dimensionless) Ground-state probability in an infinite well Stated analytical example Worked point: (0.5, 2)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 2. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 2 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Particle-in-a-box model · Example 1

Ground-state probability in an infinite well

Problem & parameters. A particle is confined by infinite walls at x = 0 and L. Find the normalized ground-state probability density.

L∣ψ1∣2=2sin⁡2(πx/L)L|\psi_1|^2=2\sin^2(\pi x/L)

Solution. The walls select ψ = A sin(πx/L). Normalization gives A = √(2/L); square the wavefunction to obtain the plotted density.

Particle-in-a-box model: Ground-state probability in an infinite well. Horizontal axis: Position x / L (dimensionless). Vertical axis: Probability density × L (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.5 1.0 1.5 2.0 Probability density × L (dimensionless) Ground-state probability in an infinite well Stated analytical example Worked point: (0.5, 2)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 2. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 2 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Dirac model · Example 1

Positive free-particle energy

Problem & parameters. For a free massive Dirac particle, evaluate the positive-energy branch versus momentum.

E/(mc2)=1+(p/mc)2E/(mc^2)=\sqrt{1+(p/mc)^2}

Solution. Squaring the free Dirac Hamiltonian gives E² = m²c⁴+p²c². Select its positive root.

Dirac model: Positive free-particle energy. Horizontal axis: Momentum p / mc (dimensionless). Vertical axis: Energy E / mc² (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Momentum p / mc (dimensionless) 1.0 1.5 2.0 2.5 3.0 Energy E / mc² (dimensionless) Positive free-particle energy Stated analytical example Worked point: (1.5, 1.803)
Orange point: horizontal coordinate 1.5, calculated vertical coordinate 1.8028. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5, into the displayed formula to obtain 1.8028 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Born–Oppenheimer approximation · Example 1

Nuclear motion on a harmonic energy surface

Problem & parameters. Approximate one Born–Oppenheimer potential-energy surface near its minimum by a spring of stiffness k.

(U−U0)/(kℓ2)=12q2,q=(R−Re)/ℓ(U-U_0)/(k\ell^2)=\tfrac12q^2,\quad q=(R-R_e)/\ell

Solution. Taylor-expand the electronic energy about its minimum. The linear term vanishes; retain the quadratic term.

Born–Oppenheimer approximation: Nuclear motion on a harmonic energy surface. Horizontal axis: Bond displacement / length scale (dimensionless). Vertical axis: Energy above minimum / kℓ² (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 Bond displacement / length scale (dimensionless) 0.0 0.5 1.0 1.5 2.0 Energy above minimum / kℓ² (dimensionless) Nuclear motion on a harmonic energy surface Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Local harmonic approximation on a single adiabatic surface; electronic crossings and nonadiabatic coupling are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Hartree–Fock model · Example 1

One-electron hydrogenic radial probability

Problem & parameters. Use the normalized hydrogen 1s state for one electron in a Coulomb potential. Plot probability per radial interval.

a0P(r)=4(r/a0)2e−2r/a0a_0P(r)=4(r/a_0)^2e^{-2r/a_0}

Solution. The 1s density is exp(−2r/a₀)/(πa₀³). Multiply by the spherical volume factor 4πr².

Hartree–Fock model: One-electron hydrogenic radial probability. Horizontal axis: Radius r / a₀ (dimensionless). Vertical axis: Radial probability density × a₀ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Radius r / a₀ (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Radial probability density × a₀ (dimensionless) One-electron hydrogenic radial probability Stated analytical example Worked point: (3, 0.08924)
Orange point: horizontal coordinate 3, calculated vertical coordinate 0.089235. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3, into the displayed formula to obtain 0.089235 on the vertical axis. Values are rounded for display.

Scope. Hartree–Fock is exact for this one-electron case. For DFT this is an exact-functional reference; approximate functionals need not reproduce it exactly.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Density functional theory (DFT) · Example 1

One-electron hydrogenic radial probability

Problem & parameters. Use the normalized hydrogen 1s state for one electron in a Coulomb potential. Plot probability per radial interval.

a0P(r)=4(r/a0)2e−2r/a0a_0P(r)=4(r/a_0)^2e^{-2r/a_0}

Solution. The 1s density is exp(−2r/a₀)/(πa₀³). Multiply by the spherical volume factor 4πr².

Density functional theory (DFT): One-electron hydrogenic radial probability. Horizontal axis: Radius r / a₀ (dimensionless). Vertical axis: Radial probability density × a₀ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Radius r / a₀ (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Radial probability density × a₀ (dimensionless) One-electron hydrogenic radial probability Stated analytical example Worked point: (3, 0.08924)
Orange point: horizontal coordinate 3, calculated vertical coordinate 0.089235. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3, into the displayed formula to obtain 0.089235 on the vertical axis. Values are rounded for display.

Scope. Hartree–Fock is exact for this one-electron case. For DFT this is an exact-functional reference; approximate functionals need not reproduce it exactly.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Time-dependent DFT (TDDFT) · Example 1

A coherent two-state population

Problem & parameters. Consider a resonantly driven, noninteracting two-level reference starting in its lower state.

P2(t)=sin⁡2(Ωt/2)P_2(t)=\sin^2(\Omega t/2)

Solution. Solve the resonant two-amplitude system to obtain upper-state amplitude −i sin(Ωt/2), then take its squared magnitude.

Time-dependent DFT (TDDFT): A coherent two-state population. Horizontal axis: Rabi angle Ωt (radian). Vertical axis: Excited-state population (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Rabi angle Ωt (radian) 0.0 0.2 0.4 0.6 0.8 1.0 Excited-state population (dimensionless) A coherent two-state population Stated analytical example Worked point: (3.142, 1)
Orange point: horizontal coordinate 3.1416, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3.1416, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Two-level rotating-wave reference for time-dependent electronic calculations; not a general TDDFT solution.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Tight-binding model · Example 1

Nearest-neighbor chain band

Problem & parameters. An infinite one-orbital chain has nearest-neighbor hopping tₕ and zero on-site energy.

E(k)/th=−2cos⁡(ka)E(k)/t_h=-2\cos(ka)

Solution. Insert a Bloch state exp(ikna) into the hopping equation; the two neighbors contribute −tₕ(exp(ika)+exp(−ika)).

Tight-binding model: Nearest-neighbor chain band. Horizontal axis: Wave number × lattice spacing ka (radian). Vertical axis: Band energy / hopping tₕ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −3 −2 −1 0 1 2 3 Wave number × lattice spacing ka (radian) −2 −1 0 1 2 Band energy / hopping tₕ (dimensionless) Nearest-neighbor chain band Stated analytical example Worked point: (0, -2)
Orange point: horizontal coordinate 0, calculated vertical coordinate -2. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain -2 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Hubbard model · Example 1

Hubbard dimer singlet ground energy

Problem & parameters. Find the two-electron singlet ground energy of a two-site Hubbard dimer with hopping tₕ > 0 and repulsion U.

E0/th=12[u−u2+16],u=U/thE_0/t_h=\tfrac12[u-\sqrt{u^2+16}],\quad u=U/t_h

Solution. In the coupled singlet/double-occupancy block the matrix has diagonal 0,U and off-diagonal −2tₕ. Solve its quadratic characteristic equation and select the lower eigenvalue.

Hubbard model: Hubbard dimer singlet ground energy. Horizontal axis: Repulsion U / hopping tₕ (dimensionless). Vertical axis: Ground energy E₀ / tₕ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 2 4 6 8 10 12 Repulsion U / hopping tₕ (dimensionless) −2.00 −1.75 −1.50 −1.25 −1.00 −0.75 −0.50 −0.25 Ground energy E₀ / tₕ (dimensionless) Hubbard dimer singlet ground energy Stated analytical example Worked point: (6, -0.6056)
Orange point: horizontal coordinate 6, calculated vertical coordinate -0.60555. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 6, into the displayed formula to obtain -0.60555 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Heisenberg spin model · Example 1

Two classical spins

Problem & parameters. Two classical unit spins interact through −J s₁·s₂ with J > 0.

E/J=−cos⁡θE/J=-\cos\theta

Solution. The dot product of two unit vectors is cos θ. Parallel alignment minimizes the energy.

Heisenberg spin model: Two classical spins. Horizontal axis: Relative spin angle θ (radian). Vertical axis: Energy / exchange J (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Relative spin angle θ (radian) −1.0 −0.5 0.0 0.5 1.0 Energy / exchange J (dimensionless) Two classical spins Stated analytical example Worked point: (1.571, -6.123e-17)
Orange point: horizontal coordinate 1.5708, calculated vertical coordinate -6.1232e-17. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5708, into the displayed formula to obtain -6.1232e-17 on the vertical axis. Values are rounded for display.

Scope. Classical two-spin special case; quantum spin spectra require a different treatment.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Ising model · Example 1

One Ising spin in a field

Problem & parameters. A single spin s = ±1 has energy −hs at inverse temperature β. Find its thermal mean.

⟨s⟩=tanh⁡(βh)\langle s\rangle=\tanh(\beta h)

Solution. Its partition function is 2 cosh(βh). The weighted spin sum is 2 sinh(βh); divide to obtain tanh(βh).

Ising model: One Ising spin in a field. Horizontal axis: Field / thermal energy βh (dimensionless). Vertical axis: Mean spin (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −3 −2 −1 0 1 2 3 Field / thermal energy βh (dimensionless) −1.0 −0.5 0.0 0.5 1.0 Mean spin (dimensionless) One Ising spin in a field Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Quantum harmonic oscillator · Example 1

Oscillator ground-state density

Problem & parameters. Use oscillator length ℓ = √(ℏ/mω) and find the normalized ground-state density.

ℓ∣ψ0∣2=π−1/2e−(x/ℓ)2\ell|\psi_0|^2=\pi^{-1/2}e^{-(x/\ell)^2}

Solution. Substitute a Gaussian into the stationary Schrödinger equation. The ground-state wavefunction is exp(−x²/2ℓ²)/(π¼√ℓ); square it.

Quantum harmonic oscillator: Oscillator ground-state density. Horizontal axis: Position x / oscillator length ℓ (dimensionless). Vertical axis: Probability density × ℓ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −3 −2 −1 0 1 2 3 Position x / oscillator length ℓ (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Probability density × ℓ (dimensionless) Oscillator ground-state density Stated analytical example Worked point: (0, 0.5642)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0.56419. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0.56419 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Classical molecular dynamics (MD) · Example 1

Isolated harmonic vibration

Problem & parameters. Take one isolated coordinate with potential kq²/2, initial displacement A, and zero initial velocity.

q(τ)=cos⁡τq(\tau)=\cos\tau

Solution. Newton’s equation reduces to q″+ω²q = 0. The initial data select A cos(ωt).

Classical molecular dynamics (MD): Isolated harmonic vibration. Horizontal axis: Time × natural frequency ωt (radian). Vertical axis: Bond displacement / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Time × natural frequency ωt (radian) −1.0 −0.5 0.0 0.5 1.0 Bond displacement / initial amplitude (dimensionless) Isolated harmonic vibration Stated analytical example Worked point: (3.142, -1)
Orange point: horizontal coordinate 3.1416, calculated vertical coordinate -1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3.1416, into the displayed formula to obtain -1 on the vertical axis. Values are rounded for display.

Scope. Harmonic force benchmark for MD or locally harmonic ab initio dynamics; real many-atom trajectories are not generally sinusoidal.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Ab initio molecular dynamics · Example 1

Isolated harmonic vibration

Problem & parameters. Take one isolated coordinate with potential kq²/2, initial displacement A, and zero initial velocity.

q(τ)=cos⁡τq(\tau)=\cos\tau

Solution. Newton’s equation reduces to q″+ω²q = 0. The initial data select A cos(ωt).

Ab initio molecular dynamics: Isolated harmonic vibration. Horizontal axis: Time × natural frequency ωt (radian). Vertical axis: Bond displacement / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Time × natural frequency ωt (radian) −1.0 −0.5 0.0 0.5 1.0 Bond displacement / initial amplitude (dimensionless) Isolated harmonic vibration Stated analytical example Worked point: (3.142, -1)
Orange point: horizontal coordinate 3.1416, calculated vertical coordinate -1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3.1416, into the displayed formula to obtain -1 on the vertical axis. Values are rounded for display.

Scope. Harmonic force benchmark for MD or locally harmonic ab initio dynamics; real many-atom trajectories are not generally sinusoidal.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Lennard–Jones potential · Example 1

Lennard–Jones pair contribution

Problem & parameters. Evaluate an unshifted 12–6 pair potential at reduced separation r/σ.

U/ε=4[(σ/r)12−(σ/r)6]U/\varepsilon=4[(\sigma/r)^{12}-(\sigma/r)^6]

Solution. Insert the reduced distance into the two inverse powers. Differentiating gives a minimum at r/σ = 2^(1/6), with U/ε = −1.

Lennard–Jones potential: Lennard–Jones pair contribution. Horizontal axis: Separation r / σ (dimensionless). Vertical axis: Pair energy U / ε (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 Separation r / σ (dimensionless) −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 Pair energy U / ε (dimensionless) Lennard–Jones pair contribution Stated analytical example Worked point: (1.975, -0.06626)
Orange point: horizontal coordinate 1.975, calculated vertical coordinate -0.066264. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.975, into the displayed formula to obtain -0.066264 on the vertical axis. Values are rounded for display.

Scope. For water and Martini entries, this is only a Lennard–Jones interaction contribution; electrostatics, constraints, and other sites are not included.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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SPC/E water model · Example 1

Lennard–Jones pair contribution

Problem & parameters. Evaluate an unshifted 12–6 pair potential at reduced separation r/σ.

U/ε=4[(σ/r)12−(σ/r)6]U/\varepsilon=4[(\sigma/r)^{12}-(\sigma/r)^6]

Solution. Insert the reduced distance into the two inverse powers. Differentiating gives a minimum at r/σ = 2^(1/6), with U/ε = −1.

SPC/E water model: Lennard–Jones pair contribution. Horizontal axis: Separation r / σ (dimensionless). Vertical axis: Pair energy U / ε (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 Separation r / σ (dimensionless) −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 Pair energy U / ε (dimensionless) Lennard–Jones pair contribution Stated analytical example Worked point: (1.975, -0.06626)
Orange point: horizontal coordinate 1.975, calculated vertical coordinate -0.066264. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.975, into the displayed formula to obtain -0.066264 on the vertical axis. Values are rounded for display.

Scope. For water and Martini entries, this is only a Lennard–Jones interaction contribution; electrostatics, constraints, and other sites are not included.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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TIP4P water-model family · Example 1

Lennard–Jones pair contribution

Problem & parameters. Evaluate an unshifted 12–6 pair potential at reduced separation r/σ.

U/ε=4[(σ/r)12−(σ/r)6]U/\varepsilon=4[(\sigma/r)^{12}-(\sigma/r)^6]

Solution. Insert the reduced distance into the two inverse powers. Differentiating gives a minimum at r/σ = 2^(1/6), with U/ε = −1.

TIP4P water-model family: Lennard–Jones pair contribution. Horizontal axis: Separation r / σ (dimensionless). Vertical axis: Pair energy U / ε (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 Separation r / σ (dimensionless) −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 Pair energy U / ε (dimensionless) Lennard–Jones pair contribution Stated analytical example Worked point: (1.975, -0.06626)
Orange point: horizontal coordinate 1.975, calculated vertical coordinate -0.066264. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.975, into the displayed formula to obtain -0.066264 on the vertical axis. Values are rounded for display.

Scope. For water and Martini entries, this is only a Lennard–Jones interaction contribution; electrostatics, constraints, and other sites are not included.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Martini coarse-grained model · Example 1

Lennard–Jones pair contribution

Problem & parameters. Evaluate an unshifted 12–6 pair potential at reduced separation r/σ.

U/ε=4[(σ/r)12−(σ/r)6]U/\varepsilon=4[(\sigma/r)^{12}-(\sigma/r)^6]

Solution. Insert the reduced distance into the two inverse powers. Differentiating gives a minimum at r/σ = 2^(1/6), with U/ε = −1.

Martini coarse-grained model: Lennard–Jones pair contribution. Horizontal axis: Separation r / σ (dimensionless). Vertical axis: Pair energy U / ε (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 Separation r / σ (dimensionless) −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 Pair energy U / ε (dimensionless) Lennard–Jones pair contribution Stated analytical example Worked point: (1.975, -0.06626)
Orange point: horizontal coordinate 1.975, calculated vertical coordinate -0.066264. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.975, into the displayed formula to obtain -0.066264 on the vertical axis. Values are rounded for display.

Scope. For water and Martini entries, this is only a Lennard–Jones interaction contribution; electrostatics, constraints, and other sites are not included.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Morse potential · Example 1

Morse bond stretching

Problem & parameters. Evaluate a Morse bond with its dissociation limit set to zero.

U/De=[1−e−q]2−1,q=a(r−re)U/D_e=[1-e^{-q}]^2-1,\quad q=a(r-r_e)

Solution. At q = 0 the energy is −Dₑ. As q increases, the exponential tends to zero and the energy approaches the dissociation limit.

Morse potential: Morse bond stretching. Horizontal axis: Bond extension a(r−rₑ) (dimensionless). Vertical axis: Energy U / Dₑ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 Bond extension a(r−rₑ) (dimensionless) −1.0 −0.8 −0.6 −0.4 −0.2 0.0 Energy U / Dₑ (dimensionless) Morse bond stretching Stated analytical example Worked point: (1.7, -0.332)
Orange point: horizontal coordinate 1.7, calculated vertical coordinate -0.33199. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.7, into the displayed formula to obtain -0.33199 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Embedded-atom method (EAM) · Example 1

Illustrative embedding-energy contribution

Problem & parameters. Choose the illustrative embedding function F = −E*√(ρ/ρ*). Plot its density dependence.

F(ρ)/E∗=−ρ/ρ∗F(\rho)/E_*=-\sqrt{\rho/\rho_*}

Solution. Substitute the normalized local density into the chosen function. This evaluates the embedding contribution before summing pair terms.

Embedded-atom method (EAM): Illustrative embedding-energy contribution. Horizontal axis: Local density ρ / ρ* (dimensionless). Vertical axis: Embedding energy F / E* (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Local density ρ / ρ* (dimensionless) −2.0 −1.5 −1.0 −0.5 0.0 Embedding energy F / E* (dimensionless) Illustrative embedding-energy contribution Stated analytical example Worked point: (2.005, -1.416)
Orange point: horizontal coordinate 2.005, calculated vertical coordinate -1.416. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.005, into the displayed formula to obtain -1.416 on the vertical axis. Values are rounded for display.

Scope. Illustrative EAM-type embedding function; not a fitted material parameterization. MEAM angular screening and density corrections are held fixed.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Modified embedded-atom method (MEAM) · Example 1

Illustrative embedding-energy contribution

Problem & parameters. Choose the illustrative embedding function F = −E*√(ρ/ρ*). Plot its density dependence.

F(ρ)/E∗=−ρ/ρ∗F(\rho)/E_*=-\sqrt{\rho/\rho_*}

Solution. Substitute the normalized local density into the chosen function. This evaluates the embedding contribution before summing pair terms.

Modified embedded-atom method (MEAM): Illustrative embedding-energy contribution. Horizontal axis: Local density ρ / ρ* (dimensionless). Vertical axis: Embedding energy F / E* (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Local density ρ / ρ* (dimensionless) −2.0 −1.5 −1.0 −0.5 0.0 Embedding energy F / E* (dimensionless) Illustrative embedding-energy contribution Stated analytical example Worked point: (2.005, -1.416)
Orange point: horizontal coordinate 2.005, calculated vertical coordinate -1.416. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.005, into the displayed formula to obtain -1.416 on the vertical axis. Values are rounded for display.

Scope. Illustrative EAM-type embedding function; not a fitted material parameterization. MEAM angular screening and density corrections are held fixed.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Tersoff bond-order potential · Example 1

A frozen bond-order pair

Problem & parameters. In a Tersoff-form pair term, hold cutoff and bond order at one and choose two exponential terms with coefficients 1 and 2.

U/E∗=e−2q−2e−qU/E_*=e^{-2q}-2e^{-q}

Solution. Substitute the fixed bond order into the repulsive-minus-attractive energy. Differentiate the resulting two exponentials to inspect the force.

Tersoff bond-order potential: A frozen bond-order pair. Horizontal axis: Reduced separation q (dimensionless). Vertical axis: Pair energy / E* (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Reduced separation q (dimensionless) −1.0 −0.8 −0.6 −0.4 −0.2 0.0 Pair energy / E* (dimensionless) A frozen bond-order pair Stated analytical example Worked point: (2, -0.2524)
Orange point: horizontal coordinate 2, calculated vertical coordinate -0.25235. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2, into the displayed formula to obtain -0.25235 on the vertical axis. Values are rounded for display.

Scope. Toy fixed-environment pair contribution; this excludes environment-dependent bond order and cutoff transitions.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Stillinger–Weber potential · Example 1

Tetrahedral angular penalty

Problem & parameters. Hold the radial factor of a Stillinger–Weber three-body term fixed and vary the included angle.

U3/K=(cos⁡θ+1/3)2U_3/K=(\cos\theta+1/3)^2

Solution. The squared angular factor vanishes at cos θ = −1/3, giving the tetrahedral angle.

Stillinger–Weber potential: Tetrahedral angular penalty. Horizontal axis: Bond angle θ (radian). Vertical axis: Angular energy / K (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Bond angle θ (radian) 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 Angular energy / K (dimensionless) Tetrahedral angular penalty Stated analytical example Worked point: (1.571, 0.1111)
Orange point: horizontal coordinate 1.5708, calculated vertical coordinate 0.11111. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5708, into the displayed formula to obtain 0.11111 on the vertical axis. Values are rounded for display.

Scope. Angular contribution only, with fixed radial prefactor K > 0.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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ReaxFF reactive force field · Example 1

Local harmonic bond-energy example

Problem & parameters. Near a stable isolated bond minimum, use the local quadratic energy with curvature k > 0.

ΔU/(kℓ2)=q2/2\Delta U/(k\ell^2)=q^2/2

Solution. The energy gradient vanishes at equilibrium. Retaining the second Taylor derivative gives ΔU = k(Δr)²/2.

ReaxFF reactive force field: Local harmonic bond-energy example. Horizontal axis: Bond extension / ℓ (dimensionless). Vertical axis: Energy increment / kℓ² (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Bond extension / ℓ (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 Energy increment / kℓ² (dimensionless) Local harmonic bond-energy example Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Local Taylor benchmark, not the full force field or a trained potential prediction; reactive changes and other coordinates are held fixed.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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AMBER force-field family · Example 1

Local harmonic bond-energy example

Problem & parameters. Near a stable isolated bond minimum, use the local quadratic energy with curvature k > 0.

ΔU/(kℓ2)=q2/2\Delta U/(k\ell^2)=q^2/2

Solution. The energy gradient vanishes at equilibrium. Retaining the second Taylor derivative gives ΔU = k(Δr)²/2.

AMBER force-field family: Local harmonic bond-energy example. Horizontal axis: Bond extension / ℓ (dimensionless). Vertical axis: Energy increment / kℓ² (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Bond extension / ℓ (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 Energy increment / kℓ² (dimensionless) Local harmonic bond-energy example Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Local Taylor benchmark, not the full force field or a trained potential prediction; reactive changes and other coordinates are held fixed.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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CHARMM force-field family · Example 1

Local harmonic bond-energy example

Problem & parameters. Near a stable isolated bond minimum, use the local quadratic energy with curvature k > 0.

ΔU/(kℓ2)=q2/2\Delta U/(k\ell^2)=q^2/2

Solution. The energy gradient vanishes at equilibrium. Retaining the second Taylor derivative gives ΔU = k(Δr)²/2.

CHARMM force-field family: Local harmonic bond-energy example. Horizontal axis: Bond extension / ℓ (dimensionless). Vertical axis: Energy increment / kℓ² (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Bond extension / ℓ (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 Energy increment / kℓ² (dimensionless) Local harmonic bond-energy example Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Local Taylor benchmark, not the full force field or a trained potential prediction; reactive changes and other coordinates are held fixed.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Machine-learned interatomic potential · Example 1

Local harmonic bond-energy example

Problem & parameters. Near a stable isolated bond minimum, use the local quadratic energy with curvature k > 0.

ΔU/(kℓ2)=q2/2\Delta U/(k\ell^2)=q^2/2

Solution. The energy gradient vanishes at equilibrium. Retaining the second Taylor derivative gives ΔU = k(Δr)²/2.

Machine-learned interatomic potential: Local harmonic bond-energy example. Horizontal axis: Bond extension / ℓ (dimensionless). Vertical axis: Energy increment / kℓ² (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Bond extension / ℓ (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 Energy increment / kℓ² (dimensionless) Local harmonic bond-energy example Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Local Taylor benchmark, not the full force field or a trained potential prediction; reactive changes and other coordinates are held fixed.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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OPLS force-field family · Example 1

One torsional Fourier term

Problem & parameters. Retain only the first OPLS torsion coefficient V₁.

U/V1=12(1+cos⁡ϕ)U/V_1=\tfrac12(1+\cos\phi)

Solution. Set the other Fourier coefficients to zero and evaluate the remaining cosine term.

OPLS force-field family: One torsional Fourier term. Horizontal axis: Dihedral angle φ (radian). Vertical axis: Torsion energy / V₁ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Dihedral angle φ (radian) 0.0 0.2 0.4 0.6 0.8 1.0 Torsion energy / V₁ (dimensionless) One torsional Fourier term Stated analytical example Worked point: (3.142, 0)
Orange point: horizontal coordinate 3.1416, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3.1416, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Single torsional energy contribution, not the full molecular force field.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Drude polarizable model · Example 1

Induced dipole in a uniform field

Problem & parameters. A charged Drude oscillator has harmonic stiffness k and charge q. Find its static induced dipole.

p/(αE∗)=E/E∗p/(\alpha E_*)=E/E_*

Solution. Balance kx = qE. Then p = qx = (q²/k)E, so α = q²/k.

Drude polarizable model: Induced dipole in a uniform field. Horizontal axis: Electric field E / E* (dimensionless). Vertical axis: Dipole p / αE* (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 Electric field E / E* (dimensionless) −2 −1 0 1 2 Dipole p / αE* (dimensionless) Induced dipole in a uniform field Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Coarse-grained molecular model · Example 1

Gaussian coarse-coordinate free energy

Problem & parameters. Let a coarse variable have Gaussian probability proportional to exp(−q²/2).

F(q)/(kBT)=q2/2F(q)/(k_BT)=q^2/2

Solution. Apply F = −kBT ln P, and remove the additive normalization constant.

Coarse-grained molecular model: Gaussian coarse-coordinate free energy. Horizontal axis: Coarse coordinate / standard deviation (dimensionless). Vertical axis: Free energy / kBT (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −3 −2 −1 0 1 2 3 Coarse coordinate / standard deviation (dimensionless) 0 1 2 3 4 5 Free energy / kBT (dimensionless) Gaussian coarse-coordinate free energy Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Exactly solvable Gaussian coarse-graining example; it does not assert that arbitrary coarse models are harmonic.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Dissipative particle dynamics (DPD) · Example 1

Mean relative velocity under fixed pair drag

Problem & parameters. Hold pair distance and weight fixed; the mean relative velocity obeys dy/dτ = −y. Random force has zero mean.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Dissipative particle dynamics (DPD): Mean relative velocity under fixed pair drag. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Mean relative velocity / initial mean (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Mean relative velocity / initial mean (dimensionless) Mean relative velocity under fixed pair drag Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Mean of a linear frozen-geometry pair reduction. DPD sample trajectories fluctuate and require a stochastic integrator.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Brownian dynamics · Example 1

One-dimensional mean-square displacement

Problem & parameters. For free Brownian motion in one dimension take D = 1 m²/s and initial position zero.

⟨[x(t)−x(0)]2⟩=2Dt\langle[x(t)-x(0)]^2\rangle=2Dt

Solution. Integrate dx = √(2D)dW. Since the variance of W(t) is t, the mean-square displacement is 2Dt.

Brownian dynamics: One-dimensional mean-square displacement. Horizontal axis: Time t (s). Vertical axis: Mean-square displacement (m²). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time t (s) 0 2 4 6 8 10 Mean-square displacement (m²) One-dimensional mean-square displacement Stated analytical example Worked point: (2.5, 5)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 5 on the vertical axis. Values are rounded for display.

Scope. Ensemble expectation, not a single random trajectory; illustrative diffusivity.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Langevin dynamics · Example 1

Mean velocity after an impulse

Problem & parameters. A free Langevin particle has linear drag γ, mass m, mean initial speed v₀, and zero-mean thermal noise. Use τ = γt/m.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Langevin dynamics: Mean velocity after an impulse. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Mean velocity / initial mean velocity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Mean velocity / initial mean velocity (dimensionless) Mean velocity after an impulse Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Ensemble mean velocity; the plotted smooth decay is not an individual noisy trajectory.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Kinetic Monte Carlo · Example 1

Probability of a first event

Problem & parameters. A kinetic Monte Carlo process has one constant total escape rate λ. Find the probability that its first event has occurred.

P(T≤t)=1−e−λtP(T\le t)=1-e^{-\lambda t}

Solution. The survival probability solves S′ = −λS with S(0) = 1. Subtract S from one.

Kinetic Monte Carlo: Probability of a first event. Horizontal axis: Elapsed hazard λt (dimensionless). Vertical axis: Event probability (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Elapsed hazard λt (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Event probability (dimensionless) Probability of a first event Stated analytical example Worked point: (2.5, 0.9179)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.91792. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.91792 on the vertical axis. Values are rounded for display.

Scope. Waiting-time distribution for a fixed state and rate, not the entire evolving event network.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Cahn–Hilliard model · Example 1

A linear conserved-composition mode

Problem & parameters. Use dimensionless Cahn–Hilliard dynamics with M = a = κ = 1, quadratic free energy ac²/2, periodic boundaries, and initial perturbation cos x. Plot t = 1.

c−cˉ=e−2cos⁡xc-\bar c=e^{-2}\cos x

Solution. For wave number one, the amplitude satisfies A′ = −M(a+κ)A = −2A. Thus A(1) = exp(−2).

Cahn–Hilliard model: A linear conserved-composition mode. Horizontal axis: Position x (dimensionless). Vertical axis: Composition perturbation (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Position x (dimensionless) −0.15 −0.10 −0.05 0.00 0.05 0.10 0.15 Composition perturbation (dimensionless) A linear conserved-composition mode Stated analytical example Worked point: (3.142, -0.1353)
Orange point: horizontal coordinate 3.1416, calculated vertical coordinate -0.13534. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3.1416, into the displayed formula to obtain -0.13534 on the vertical axis. Values are rounded for display.

Scope. Exact quadratic-free-energy special case, not nonlinear phase separation.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Allen–Cahn model · Example 1

A relaxing Allen–Cahn mode

Problem & parameters. Take mobility, positive quadratic free-energy curvature, and gradient coefficient all equal to one, with initial cos x.

η(x,1)=e−2cos⁡x\eta(x,1)=e^{-2}\cos x

Solution. The local and gradient terms each contribute −A to the amplitude equation. Integrate A′ = −2A.

Allen–Cahn model: A relaxing Allen–Cahn mode. Horizontal axis: Position x (dimensionless). Vertical axis: Order parameter η (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Position x (dimensionless) −0.15 −0.10 −0.05 0.00 0.05 0.10 0.15 Order parameter η (dimensionless) A relaxing Allen–Cahn mode Stated analytical example Worked point: (3.142, -0.1353)
Orange point: horizontal coordinate 3.1416, calculated vertical coordinate -0.13534. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3.1416, into the displayed formula to obtain -0.13534 on the vertical axis. Values are rounded for display.

Scope. Linear quadratic-free-energy special case; domain walls of a double-well model are not represented.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Phase-field crystal model · Example 1

Linearized phase-field-crystal mode

Problem & parameters. Linearize ∂tψ = ∇²[(r+(1+∇²)²)ψ+ψ³] about ψ = 0 with r = 1; initial amplitude A₀ = 0.01 and wave number one.

δψ(x,1)=A0e−1cos⁡x\delta\psi(x,1)=A_0e^{-1}\cos x

Solution. The operator (1+∂xx) annihilates cos x. The remaining linear amplitude equation is A′ = −A.

Phase-field crystal model: Linearized phase-field-crystal mode. Horizontal axis: Position x (dimensionless). Vertical axis: Density perturbation δψ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Position x (dimensionless) −0.004 −0.002 0.000 0.002 0.004 Density perturbation δψ (dimensionless) Linearized phase-field-crystal mode Stated analytical example Worked point: (3.142, -0.003679)
Orange point: horizontal coordinate 3.1416, calculated vertical coordinate -0.0036788. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3.1416, into the displayed formula to obtain -0.0036788 on the vertical axis. Values are rounded for display.

Scope. Linearized small-perturbation solution; the cubic term is omitted.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Potts grain-growth model · Example 1

Two-site Potts equilibrium alignment

Problem & parameters. For a three-state two-site Potts pair with energy −J when the states agree, compute the equilibrium agreement probability.

Psame=eueu+q−1,q=3P_{\rm same}=\frac{e^u}{e^u+q-1},\quad q=3

Solution. There are q agreeing states with Boltzmann weight exp(J/kBT), and q(q−1) disagreeing states with weight one. Normalize their sums.

Potts grain-growth model: Two-site Potts equilibrium alignment. Horizontal axis: Coupling / thermal energy J/kBT (dimensionless). Vertical axis: Alignment probability (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Coupling / thermal energy J/kBT (dimensionless) 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Alignment probability (dimensionless) Two-site Potts equilibrium alignment Stated analytical example Worked point: (2.5, 0.859)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.85898. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.85898 on the vertical axis. Values are rounded for display.

Scope. Finite equilibrium toy problem, not a simulated grain-growth history.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Discrete dislocation dynamics · Example 1

Straight dislocation with constant mobility

Problem & parameters. Take one straight segment, constant force per length f = 1 N/m and mobility M = 1 m²/(N·s), starting at x = 0.

x(t)=Mftx(t)=Mft

Solution. The overdamped mobility law gives constant velocity Mf; integrate with the initial position.

Discrete dislocation dynamics: Straight dislocation with constant mobility. Horizontal axis: Time t (s). Vertical axis: Dislocation displacement (m). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time t (s) 0 1 2 3 4 5 Dislocation displacement (m) Straight dislocation with constant mobility Stated analytical example Worked point: (2.5, 2.5)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 2.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 2.5 on the vertical axis. Values are rounded for display.

Scope. Illustrative coefficients; interactions, pinning, and changing segment geometry are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Population balance model · Example 1

Translated size distribution

Problem & parameters. For ∂tn+∂sn = 0 use n(s,0) = exp[−(s−2)²], constant growth G = 1, and compatible boundary inflow. Plot t = 1.

n(s,1)=e−(s−3)2n(s,1)=e^{-(s-3)^2}

Solution. Along characteristics s−t is constant. Therefore n(s,t) = n₀(s−t).

Population balance model: Translated size distribution. Horizontal axis: Particle size s (dimensionless). Vertical axis: Number-density profile (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Particle size s (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Number-density profile (dimensionless) Translated size distribution Stated analytical example Worked point: (3, 1)
Orange point: horizontal coordinate 3, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Ideal gas equation of state · Example 1

An ideal-gas isotherm

Problem & parameters. Hold temperature and amount of ideal gas fixed while varying its volume.

pV∗/(nRT)=1/(V/V∗)pV_*/(nRT)=1/(V/V_*)

Solution. Solve pV = nRT for pressure and divide by the reference pressure nRT/V*.

Ideal gas equation of state: An ideal-gas isotherm. Horizontal axis: Volume V / V* (dimensionless). Vertical axis: Pressure pV* / nRT (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 1 2 3 4 5 Volume V / V* (dimensionless) 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 Pressure pV* / nRT (dimensionless) An ideal-gas isotherm Stated analytical example Worked point: (2.75, 0.3636)
Orange point: horizontal coordinate 2.75, calculated vertical coordinate 0.36364. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.75, into the displayed formula to obtain 0.36364 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Van der Waals equation of state · Example 1

A supercritical van der Waals isotherm

Problem & parameters. Use the reduced van der Waals equation at T/Tc = 1.2.

p/pc=8(1.2)3v−1−3v2p/p_c=\frac{8(1.2)}{3v-1}-\frac3{v^2}

Solution. Insert the critical scalings Vc = 3b, pc = a/(27b²), and Tc = 8a/(27Rb), then evaluate the reduced expression.

Van der Waals equation of state: A supercritical van der Waals isotherm. Horizontal axis: Molar volume Vₘ / Vc (dimensionless). Vertical axis: Pressure p / pc (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Molar volume Vₘ / Vc (dimensionless) 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Pressure p / pc (dimensionless) A supercritical van der Waals isotherm Stated analytical example Worked point: (2.3, 1.06)
Orange point: horizontal coordinate 2.3, calculated vertical coordinate 1.06. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.3, into the displayed formula to obtain 1.06 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Peng–Robinson equation of state · Example 1

Peng–Robinson fixed-temperature curve

Problem & parameters. At fixed temperature choose aα/(RTb) = 2 and evaluate the Peng–Robinson pressure.

pb/(RT)=1v−1−2v2+2v−1pb/(RT)=\frac1{v-1}-\frac2{v^2+2v-1}

Solution. Divide its repulsive and attractive terms by RT/b and substitute v = Vₘ/b.

Peng–Robinson equation of state: Peng–Robinson fixed-temperature curve. Horizontal axis: Molar volume v = Vₘ / b (dimensionless). Vertical axis: Pressure pb / RT (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 2 3 4 5 6 Molar volume v = Vₘ / b (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Pressure pb / RT (dimensionless) Peng–Robinson fixed-temperature curve Stated analytical example Worked point: (3.75, 0.2664)
Orange point: horizontal coordinate 3.75, calculated vertical coordinate 0.26637. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3.75, into the displayed formula to obtain 0.26637 on the vertical axis. Values are rounded for display.

Scope. Illustrative EOS parameters; not a fitted fluid or a phase-equilibrium calculation.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Soave–Redlich–Kwong equation of state · Example 1

Soave–Redlich–Kwong isotherm

Problem & parameters. At fixed temperature choose aα/(RTb) = 2 for the SRK equation.

pb/(RT)=1v−1−2v(v+1)pb/(RT)=\frac1{v-1}-\frac2{v(v+1)}

Solution. Divide the EOS by RT/b and evaluate both terms using the reduced molar volume.

Soave–Redlich–Kwong equation of state: Soave–Redlich–Kwong isotherm. Horizontal axis: Molar volume v = Vₘ / b (dimensionless). Vertical axis: Pressure pb / RT (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 2 3 4 5 6 Molar volume v = Vₘ / b (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Pressure pb / RT (dimensionless) Soave–Redlich–Kwong isotherm Stated analytical example Worked point: (3.75, 0.2514)
Orange point: horizontal coordinate 3.75, calculated vertical coordinate 0.25136. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3.75, into the displayed formula to obtain 0.25136 on the vertical axis. Values are rounded for display.

Scope. Illustrative parameters; the temperature dependence of α is fixed for this isotherm.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Virial equation of state · Example 1

A truncated virial compressibility

Problem & parameters. Use scaled second and third virial coefficients 0.2 and 0.05 over a dilute density interval.

Z=1+0.2ρ∗+0.05ρ∗2Z=1+0.2\rho_*+0.05\rho_*^2

Solution. Substitute the reduced density into Z = 1+Bρ+Cρ². At zero density it recovers the ideal-gas limit Z = 1.

Virial equation of state: A truncated virial compressibility. Horizontal axis: Reduced density ρ* (dimensionless). Vertical axis: Compressibility factor Z (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Reduced density ρ* (dimensionless) 1.00 1.05 1.10 1.15 1.20 1.25 Compressibility factor Z (dimensionless) A truncated virial compressibility Stated analytical example Worked point: (0.5, 1.113)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1.1125. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1.1125 on the vertical axis. Values are rounded for display.

Scope. Truncated low-density illustrative expansion, not an extrapolation to dense fluids.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Gibbs-energy minimization · Example 1

Ideal binary mixing free energy

Problem & parameters. Take an ideal binary solution with equal pure-component reference energies. Find the composition dependence of its mixing free energy.

Δg/(RT)=xln⁡x+(1−x)ln⁡(1−x)\Delta g/(RT)=x\ln x+(1-x)\ln(1-x)

Solution. Sum the two ideal mixing contributions. Differentiation gives ln[x/(1−x)] = 0, so the minimum is at x = 1/2.

Gibbs-energy minimization: Ideal binary mixing free energy. Horizontal axis: Mole fraction x (dimensionless). Vertical axis: Mixing free energy / RT (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Mole fraction x (dimensionless) −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 Mixing free energy / RT (dimensionless) Ideal binary mixing free energy Stated analytical example Worked point: (0.5, -0.6931)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate -0.69315. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain -0.69315 on the vertical axis. Values are rounded for display.

Scope. Ideal-solution Gibbs term; real CALPHAD databases include additional phase and interaction terms. Conserved bulk composition constrains accessible equilibria.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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CALPHAD model · Example 1

Ideal binary mixing free energy

Problem & parameters. Take an ideal binary solution with equal pure-component reference energies. Find the composition dependence of its mixing free energy.

Δg/(RT)=xln⁡x+(1−x)ln⁡(1−x)\Delta g/(RT)=x\ln x+(1-x)\ln(1-x)

Solution. Sum the two ideal mixing contributions. Differentiation gives ln[x/(1−x)] = 0, so the minimum is at x = 1/2.

CALPHAD model: Ideal binary mixing free energy. Horizontal axis: Mole fraction x (dimensionless). Vertical axis: Mixing free energy / RT (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Mole fraction x (dimensionless) −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 Mixing free energy / RT (dimensionless) Ideal binary mixing free energy Stated analytical example Worked point: (0.5, -0.6931)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate -0.69315. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain -0.69315 on the vertical axis. Values are rounded for display.

Scope. Ideal-solution Gibbs term; real CALPHAD databases include additional phase and interaction terms. Conserved bulk composition constrains accessible equilibria.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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NRTL activity model · Example 1

Ideal-mixture activity limit

Problem & parameters. Set NRTL interaction parameters to zero; for UNIQUAC also take identical molecular sizes and shapes with zero interaction energies.

a1=x1,γ1=1a_1=x_1,\quad\gamma_1=1

Solution. Under these restrictions the excess contribution vanishes and γ₁ = 1; activity is γ₁x₁.

NRTL activity model: Ideal-mixture activity limit. Horizontal axis: Mole fraction x₁ (dimensionless). Vertical axis: Component activity a₁ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Mole fraction x₁ (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Component activity a₁ (dimensionless) Ideal-mixture activity limit Stated analytical example Worked point: (0.5, 0.5)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.5 on the vertical axis. Values are rounded for display.

Scope. Ideal-mixture limiting case only; unequal molecular sizes in UNIQUAC can retain a combinatorial contribution.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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UNIQUAC activity model · Example 1

Ideal-mixture activity limit

Problem & parameters. Set NRTL interaction parameters to zero; for UNIQUAC also take identical molecular sizes and shapes with zero interaction energies.

a1=x1,γ1=1a_1=x_1,\quad\gamma_1=1

Solution. Under these restrictions the excess contribution vanishes and γ₁ = 1; activity is γ₁x₁.

UNIQUAC activity model: Ideal-mixture activity limit. Horizontal axis: Mole fraction x₁ (dimensionless). Vertical axis: Component activity a₁ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Mole fraction x₁ (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Component activity a₁ (dimensionless) Ideal-mixture activity limit Stated analytical example Worked point: (0.5, 0.5)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.5 on the vertical axis. Values are rounded for display.

Scope. Ideal-mixture limiting case only; unequal molecular sizes in UNIQUAC can retain a combinatorial contribution.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Debye–Hückel model · Example 1

Dilute ionic activity correction

Problem & parameters. For a monovalent ion in water near 25 °C use the Debye–Hückel limiting-law coefficient A = 0.509 (mol/L)⁻¹ᐟ².

log⁡10γ=−0.509I\log_{10}\gamma=-0.509\sqrt I

Solution. Set charge magnitude to one in log₁₀γ = −Az²√I. Evaluate only at dilute ionic strengths.

Debye–Hückel model: Dilute ionic activity correction. Horizontal axis: Ionic strength I (mol/L). Vertical axis: log₁₀(activity coefficient) (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.000 0.002 0.004 0.006 0.008 0.010 Ionic strength I (mol/L) −0.05 −0.04 −0.03 −0.02 −0.01 0.00 log₁₀(activity coefficient) (dimensionless) Dilute ionic activity correction Stated analytical example Worked point: (0.005, -0.03599)
Orange point: horizontal coordinate 0.005, calculated vertical coordinate -0.035992. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.005, into the displayed formula to obtain -0.035992 on the vertical axis. Values are rounded for display.

Scope. Limiting-law illustration; specific ion interactions and concentrated solutions are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Mass-action reaction kinetics · Example 1

First-order reactant consumption

Problem & parameters. For a single irreversible first-order reaction A → products in a constant-volume batch, use τ = kt and y = cA/cA0.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Mass-action reaction kinetics: First-order reactant consumption. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Reactant concentration / initial concentration (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Reactant concentration / initial concentration (dimensionless) First-order reactant consumption Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Exact one-mode reduction with constant coefficients; additional coupled physics is excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Batch reactor model · Example 1

First-order reactant consumption

Problem & parameters. For a single irreversible first-order reaction A → products in a constant-volume batch, use τ = kt and y = cA/cA0.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Batch reactor model: First-order reactant consumption. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Reactant concentration / initial concentration (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Reactant concentration / initial concentration (dimensionless) First-order reactant consumption Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Exact one-mode reduction with constant coefficients; additional coupled physics is excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Arrhenius rate model · Example 1

Temperature dependence of an activated rate

Problem & parameters. Hold activation energy Ea > 0 and prefactor A constant.

k/A=e−1/θ,θ=RT/Eak/A=e^{-1/\theta},\quad\theta=RT/E_a

Solution. Insert the scaled temperature into k = A exp(−Ea/RT).

Arrhenius rate model: Temperature dependence of an activated rate. Horizontal axis: Scaled temperature RT / Ea (dimensionless). Vertical axis: Rate constant / prefactor k/A (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.2 0.4 0.6 0.8 1.0 Scaled temperature RT / Ea (dimensionless) 0.0 0.1 0.2 0.3 0.4 Rate constant / prefactor k/A (dimensionless) Temperature dependence of an activated rate Stated analytical example Worked point: (0.55, 0.1623)
Orange point: horizontal coordinate 0.55, calculated vertical coordinate 0.16232. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.55, into the displayed formula to obtain 0.16232 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Transition-state theory · Example 1

Transition-state rate at fixed activation free energy

Problem & parameters. Take transmission coefficient one and treat the molar activation free energy as constant over the displayed interval.

kh/(kBT)=e−1/θ,θ=RT/ΔG‡kh/(k_BT)=e^{-1/\theta},\quad\theta=RT/\Delta G^\ddagger

Solution. Divide the Eyring expression by its kBT/h prefactor and substitute the scaled temperature.

Transition-state theory: Transition-state rate at fixed activation free energy. Horizontal axis: Scaled temperature RT / ΔG‡ (dimensionless). Vertical axis: Scaled rate kh / kBT (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.2 0.4 0.6 0.8 1.0 Scaled temperature RT / ΔG‡ (dimensionless) 0.0 0.1 0.2 0.3 0.4 Scaled rate kh / kBT (dimensionless) Transition-state rate at fixed activation free energy Stated analytical example Worked point: (0.55, 0.1623)
Orange point: horizontal coordinate 0.55, calculated vertical coordinate 0.16232. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.55, into the displayed formula to obtain 0.16232 on the vertical axis. Values are rounded for display.

Scope. Illustrative fixed-barrier curve; real activation free energy can vary with temperature.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Michaelis–Menten kinetics · Example 1

A saturating occupancy or rate

Problem & parameters. For Michaelis–Menten set x = substrate/Km and y = v/Vmax. For Langmuir adsorption set x = KP and y = occupied-site fraction.

y=x1+xy=\frac{x}{1+x}

Solution. Solve the binding or adsorption balance to give occupied fraction x/(1+x). The half-saturation point is x = 1.

Michaelis–Menten kinetics: A saturating occupancy or rate. Horizontal axis: Scaled concentration or pressure (dimensionless). Vertical axis: Fraction of saturation (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 7 8 Scaled concentration or pressure (dimensionless) 0.0 0.2 0.4 0.6 0.8 Fraction of saturation (dimensionless) A saturating occupancy or rate Stated analytical example Worked point: (4, 0.8)
Orange point: horizontal coordinate 4, calculated vertical coordinate 0.8. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 4, into the displayed formula to obtain 0.8 on the vertical axis. Values are rounded for display.

Scope. Single-substrate steady enzyme law or single-species equilibrium adsorption, as appropriate to the entry.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Langmuir adsorption isotherm · Example 1

A saturating occupancy or rate

Problem & parameters. For Michaelis–Menten set x = substrate/Km and y = v/Vmax. For Langmuir adsorption set x = KP and y = occupied-site fraction.

y=x1+xy=\frac{x}{1+x}

Solution. Solve the binding or adsorption balance to give occupied fraction x/(1+x). The half-saturation point is x = 1.

Langmuir adsorption isotherm: A saturating occupancy or rate. Horizontal axis: Scaled concentration or pressure (dimensionless). Vertical axis: Fraction of saturation (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 7 8 Scaled concentration or pressure (dimensionless) 0.0 0.2 0.4 0.6 0.8 Fraction of saturation (dimensionless) A saturating occupancy or rate Stated analytical example Worked point: (4, 0.8)
Orange point: horizontal coordinate 4, calculated vertical coordinate 0.8. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 4, into the displayed formula to obtain 0.8 on the vertical axis. Values are rounded for display.

Scope. Single-substrate steady enzyme law or single-species equilibrium adsorption, as appropriate to the entry.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Langmuir–Hinshelwood kinetics · Example 1

Competing adsorption and surface reaction

Problem & parameters. Use the illustrative Langmuir–Hinshelwood rate r/r* = x/(1+x)², with other factors held constant.

r/r∗=x(1+x)2r/r_*=\frac{x}{(1+x)^2}

Solution. Differentiate: the slope is (1−x)/(1+x)³. Thus the rate peaks at x = 1 and decreases under strong site blocking.

Langmuir–Hinshelwood kinetics: Competing adsorption and surface reaction. Horizontal axis: Scaled reactant pressure x (dimensionless). Vertical axis: Scaled surface rate r / r* (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 7 8 Scaled reactant pressure x (dimensionless) 0.00 0.05 0.10 0.15 0.20 0.25 Scaled surface rate r / r* (dimensionless) Competing adsorption and surface reaction Stated analytical example Worked point: (4, 0.16)
Orange point: horizontal coordinate 4, calculated vertical coordinate 0.16. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 4, into the displayed formula to obtain 0.16 on the vertical axis. Values are rounded for display.

Scope. One specified adsorption-limited rate law; the family contains many different mechanisms.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Fickian diffusion · Example 1

Binary concentration relaxation

Problem & parameters. Solve ∂τu = ∂ξξu with u(0,τ)=u(1,τ)=0 and initial sin(πξ), then plot τ = 0.1.

u(ξ,τ)=sin⁡(πξ)e−π2τ,τ=0.1u(\xi,\tau)=\sin(\pi\xi)e^{-\pi^2\tau},\quad\tau=0.1

Solution. The sine satisfies both zero end values. Its second derivative is −π² times itself; the amplitude solves a′ = −π²a.

Fickian diffusion: Binary concentration relaxation. Horizontal axis: Position x / L (dimensionless). Vertical axis: Concentration perturbation / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.1 0.2 0.3 0.4 Concentration perturbation / initial amplitude (dimensionless) Binary concentration relaxation Stated analytical example Worked point: (0.5, 0.3727)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.37271. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.37271 on the vertical axis. Values are rounded for display.

Scope. Fickian constant-diffusivity slab. Maxwell–Stefan reduces to this form for an ideal binary mixture with constant total concentration and diffusivity.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Maxwell–Stefan diffusion · Example 1

Binary concentration relaxation

Problem & parameters. Solve ∂τu = ∂ξξu with u(0,τ)=u(1,τ)=0 and initial sin(πξ), then plot τ = 0.1.

u(ξ,τ)=sin⁡(πξ)e−π2τ,τ=0.1u(\xi,\tau)=\sin(\pi\xi)e^{-\pi^2\tau},\quad\tau=0.1

Solution. The sine satisfies both zero end values. Its second derivative is −π² times itself; the amplitude solves a′ = −π²a.

Maxwell–Stefan diffusion: Binary concentration relaxation. Horizontal axis: Position x / L (dimensionless). Vertical axis: Concentration perturbation / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.1 0.2 0.3 0.4 Concentration perturbation / initial amplitude (dimensionless) Binary concentration relaxation Stated analytical example Worked point: (0.5, 0.3727)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.37271. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.37271 on the vertical axis. Values are rounded for display.

Scope. Fickian constant-diffusivity slab. Maxwell–Stefan reduces to this form for an ideal binary mixture with constant total concentration and diffusivity.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Advection–diffusion–reaction model · Example 1

Advected, diffused, reacting Gaussian

Problem & parameters. On the infinite line solve ut+ux = 0.1uxx−0.2u with u(x,0)=exp(−x²). Plot t = 1.

u(x,1)=e−0.21.4exp⁡[−(x−1)2/1.4]u(x,1)=\frac{e^{-0.2}}{\sqrt{1.4}}\exp[-(x-1)^2/1.4]

Solution. Advection translates the center by t. Diffusion increases the Gaussian width from 1 to 1+0.4t; first-order loss multiplies its conserved-mass diffusion solution by exp(−0.2t).

Advection–diffusion–reaction model: Advected, diffused, reacting Gaussian. Horizontal axis: Position x (dimensionless). Vertical axis: Concentration u (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −3 −2 −1 0 1 2 3 4 5 Position x (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Concentration u (dimensionless) Advected, diffused, reacting Gaussian Stated analytical example Worked point: (1, 0.692)
Orange point: horizontal coordinate 1, calculated vertical coordinate 0.69195. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1, into the displayed formula to obtain 0.69195 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Continuous stirred-tank reactor (CSTR) · Example 1

CSTR outlet versus residence time

Problem & parameters. At steady state a well-mixed reactor consumes A by a first-order reaction at rate kcA.

cout/cin=1/(1+Da)c_{\rm out}/c_{\rm in}=1/(1+\mathrm{Da})

Solution. Balance Qcin−Qcout−kVcout = 0 and solve for cout.

Continuous stirred-tank reactor (CSTR): CSTR outlet versus residence time. Horizontal axis: Damköhler number kV/Q (dimensionless). Vertical axis: Outlet / inlet concentration (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Damköhler number kV/Q (dimensionless) 0.2 0.4 0.6 0.8 1.0 Outlet / inlet concentration (dimensionless) CSTR outlet versus residence time Stated analytical example Worked point: (2.5, 0.2857)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.28571. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.28571 on the vertical axis. Values are rounded for display.

Scope. Constant-volume, isothermal, constant-flow reactor.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Plug-flow reactor (PFR) · Example 1

First-order plug-flow conversion

Problem & parameters. For an isothermal PFR with constant velocity u and first-order consumption k, use τ = kz/u and y = c/cin.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Plug-flow reactor (PFR): First-order plug-flow conversion. Horizontal axis: Axial residence coordinate kz / u (dimensionless). Vertical axis: Reactant concentration / inlet concentration (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Axial residence coordinate kz / u (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Reactant concentration / inlet concentration (dimensionless) First-order plug-flow conversion Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Exact axial concentration profile in ideal plug flow; the horizontal coordinate is residence time kz/u, not laboratory time.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Stokes creeping-flow model · Example 1

Pressure-driven laminar flow profile

Problem & parameters. Take steady, fully developed incompressible flow with constant viscosity between fixed parallel plates. For Hagen–Poiseuille use the equivalent diameter cut through a round pipe.

u/Umax⁡=1−ξ2u/U_{\max}=1-\xi^2

Solution. The axial momentum equation becomes a constant second derivative. Integrate twice and impose no slip at both walls to obtain a parabola.

Stokes creeping-flow model: Pressure-driven laminar flow profile. Horizontal axis: Transverse position / half-width (dimensionless). Vertical axis: Axial velocity / center velocity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Transverse position / half-width (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Axial velocity / center velocity (dimensionless) Pressure-driven laminar flow profile Stated analytical example Worked point: (0, 1)
Orange point: horizontal coordinate 0, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Exact laminar benchmark. Plate and pipe pressure-to-maximum-speed factors differ; the plotted normalized profile is identical. DNS here resolves this simple laminar case.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Lubrication approximation · Example 1

Pressure-driven laminar flow profile

Problem & parameters. Take steady, fully developed incompressible flow with constant viscosity between fixed parallel plates. For Hagen–Poiseuille use the equivalent diameter cut through a round pipe.

u/Umax⁡=1−ξ2u/U_{\max}=1-\xi^2

Solution. The axial momentum equation becomes a constant second derivative. Integrate twice and impose no slip at both walls to obtain a parabola.

Lubrication approximation: Pressure-driven laminar flow profile. Horizontal axis: Transverse position / half-width (dimensionless). Vertical axis: Axial velocity / center velocity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Transverse position / half-width (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Axial velocity / center velocity (dimensionless) Pressure-driven laminar flow profile Stated analytical example Worked point: (0, 1)
Orange point: horizontal coordinate 0, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Exact laminar benchmark. Plate and pipe pressure-to-maximum-speed factors differ; the plotted normalized profile is identical. DNS here resolves this simple laminar case.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Hagen–Poiseuille model · Example 1

Pressure-driven laminar flow profile

Problem & parameters. Take steady, fully developed incompressible flow with constant viscosity between fixed parallel plates. For Hagen–Poiseuille use the equivalent diameter cut through a round pipe.

u/Umax⁡=1−ξ2u/U_{\max}=1-\xi^2

Solution. The axial momentum equation becomes a constant second derivative. Integrate twice and impose no slip at both walls to obtain a parabola.

Hagen–Poiseuille model: Pressure-driven laminar flow profile. Horizontal axis: Transverse position / half-width (dimensionless). Vertical axis: Axial velocity / center velocity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Transverse position / half-width (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Axial velocity / center velocity (dimensionless) Pressure-driven laminar flow profile Stated analytical example Worked point: (0, 1)
Orange point: horizontal coordinate 0, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Exact laminar benchmark. Plate and pipe pressure-to-maximum-speed factors differ; the plotted normalized profile is identical. DNS here resolves this simple laminar case.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Direct numerical simulation (DNS) · Example 1

Pressure-driven laminar flow profile

Problem & parameters. Take steady, fully developed incompressible flow with constant viscosity between fixed parallel plates. For Hagen–Poiseuille use the equivalent diameter cut through a round pipe.

u/Umax⁡=1−ξ2u/U_{\max}=1-\xi^2

Solution. The axial momentum equation becomes a constant second derivative. Integrate twice and impose no slip at both walls to obtain a parabola.

Direct numerical simulation (DNS): Pressure-driven laminar flow profile. Horizontal axis: Transverse position / half-width (dimensionless). Vertical axis: Axial velocity / center velocity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Transverse position / half-width (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Axial velocity / center velocity (dimensionless) Pressure-driven laminar flow profile Stated analytical example Worked point: (0, 1)
Orange point: horizontal coordinate 0, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Exact laminar benchmark. Plate and pipe pressure-to-maximum-speed factors differ; the plotted normalized profile is identical. DNS here resolves this simple laminar case.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Euler flow model · Example 1

A small-amplitude sound wave

Problem & parameters. Linearize inviscid Euler flow about a uniform rest state and use a sinusoidal pressure perturbation.

u(ξ,0)=sin⁡(2πξ)u(\xi,0)=\sin(2\pi\xi)

Solution. A sinusoidal traveling-wave solution is u = sin[2π(ξ−τ)]. Set τ = 0 to obtain the plotted snapshot.

Euler flow model: A small-amplitude sound wave. Horizontal axis: Position / wavelength (dimensionless). Vertical axis: Pressure perturbation / amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position / wavelength (dimensionless) −1.0 −0.5 0.0 0.5 1.0 Pressure perturbation / amplitude (dimensionless) A small-amplitude sound wave Stated analytical example Worked point: (0.5, 1.225e-16)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1.2246e-16. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1.2246e-16 on the vertical axis. Values are rounded for display.

Scope. Linear acoustic limit of Euler flow, not a finite-amplitude compressible flow solution.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Potential-flow model · Example 1

Cylinder surface pressure

Problem & parameters. Find surface pressure for incompressible, inviscid, irrotational uniform flow around a circular cylinder without circulation.

Cp=1−4sin⁡2θC_p=1-4\sin^2\theta

Solution. Potential flow gives surface speed 2U∞ sin θ. Bernoulli’s equation then gives Cp = 1−(u/U∞)².

Potential-flow model: Cylinder surface pressure. Horizontal axis: Cylinder surface angle θ (radian). Vertical axis: Pressure coefficient Cp (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Cylinder surface angle θ (radian) −3 −2 −1 0 1 Pressure coefficient Cp (dimensionless) Cylinder surface pressure Stated analytical example Worked point: (3.142, 1)
Orange point: horizontal coordinate 3.1416, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3.1416, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. No viscosity or separation; this ideal model does not predict real cylinder drag.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Boundary-layer model · Example 1

A suddenly moving flat wall

Problem & parameters. A flat wall suddenly moves at speed U beneath an initially stationary semi-infinite viscous fluid.

u/U=erfc⁡(η),η=y/(2νt)u/U=\operatorname{erfc}(\eta),\quad\eta=y/(2\sqrt{\nu t})

Solution. With no streamwise variation, momentum reduces to diffusion. Similarity substitution and the wall/far-field conditions give the complementary error function.

Boundary-layer model: A suddenly moving flat wall. Horizontal axis: Similarity coordinate y / 2√(νt) (dimensionless). Vertical axis: Velocity u / wall speed U (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Similarity coordinate y / 2√(νt) (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Velocity u / wall speed U (dimensionless) A suddenly moving flat wall Stated analytical example Worked point: (1.5, 0.03389)
Orange point: horizontal coordinate 1.5, calculated vertical coordinate 0.033895. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5, into the displayed formula to obtain 0.033895 on the vertical axis. Values are rounded for display.

Scope. Stokes’ first problem, an unsteady boundary-layer benchmark; not the Blasius spatially developing solution.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Darcy–Weisbach model · Example 1

Pipe pressure loss versus speed

Problem & parameters. Hold the Darcy friction factor f, pipe geometry, and density fixed.

Δp/(fLρU∗2/2D)=(U/U∗)2\Delta p/(fL\rho U_*^2/2D)=(U/U_*)^2

Solution. Insert the mean speed into Darcy–Weisbach Δp = f(L/D)ρU²/2.

Darcy–Weisbach model: Pipe pressure loss versus speed. Horizontal axis: Mean speed U / U* (dimensionless). Vertical axis: Scaled pressure drop (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Mean speed U / U* (dimensionless) 0 2 4 6 8 10 Scaled pressure drop (dimensionless) Pipe pressure loss versus speed Stated analytical example Worked point: (1.5, 2.25)
Orange point: horizontal coordinate 1.5, calculated vertical coordinate 2.25. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5, into the displayed formula to obtain 2.25 on the vertical axis. Values are rounded for display.

Scope. Fixed-friction-factor illustration; f usually varies with Reynolds number and roughness.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Non-Newtonian power-law fluid · Example 1

Shear-thinning constitutive curve

Problem & parameters. Choose positive shear rates and power-law exponent n = 1/2, with reference stress K√(reference rate).

τ/τ∗=(γ˙/γ˙∗)1/2\tau/\tau_*=(\dot\gamma/\dot\gamma_*)^{1/2}

Solution. Substitute n = 1/2 into τ = Kγ̇ⁿ.

Non-Newtonian power-law fluid: Shear-thinning constitutive curve. Horizontal axis: Shear rate / reference rate (dimensionless). Vertical axis: Shear stress / reference stress (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Shear rate / reference rate (dimensionless) 0.0 0.5 1.0 1.5 2.0 Shear stress / reference stress (dimensionless) Shear-thinning constitutive curve Stated analytical example Worked point: (2, 1.414)
Orange point: horizontal coordinate 2, calculated vertical coordinate 1.4142. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2, into the displayed formula to obtain 1.4142 on the vertical axis. Values are rounded for display.

Scope. Steady shear constitutive evaluation; no low- or high-shear viscosity plateau is included.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Bingham plastic model · Example 1

Bingham imposed-stress response

Problem & parameters. Increase a nonnegative applied shear stress on an ideal Bingham material.

μpγ˙/τy=max⁡(s−1,0),s=τ/τy\mu_p\dot\gamma/\tau_y=\max(s-1,0),\quad s=\tau/\tau_y

Solution. Below yield, the shear rate is zero. Above yield, solve τ = τy+μpγ̇ for the rate.

Bingham plastic model: Bingham imposed-stress response. Horizontal axis: Applied stress / yield stress (dimensionless). Vertical axis: Scaled shear rate μpγ̇ / τy (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Applied stress / yield stress (dimensionless) 0.0 0.5 1.0 1.5 2.0 Scaled shear rate μpγ̇ / τy (dimensionless) Bingham imposed-stress response Stated analytical example Worked point: (1.5, 0.5)
Orange point: horizontal coordinate 1.5, calculated vertical coordinate 0.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5, into the displayed formula to obtain 0.5 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Herschel–Bulkley model · Example 1

Herschel–Bulkley stress curve

Problem & parameters. Use exponent n = 1/2 and define g so that Kγ̇ⁿ/τy = √g. Evaluate the yielded branch.

τ/τy=1+g1/2\tau/\tau_y=1+g^{1/2}

Solution. Insert the chosen exponent into τ = τy+Kγ̇ⁿ.

Herschel–Bulkley model: Herschel–Bulkley stress curve. Horizontal axis: Scaled positive shear rate g (dimensionless). Vertical axis: Shear stress / yield stress (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Scaled positive shear rate g (dimensionless) 1.0 1.5 2.0 2.5 3.0 Shear stress / yield stress (dimensionless) Herschel–Bulkley stress curve Stated analytical example Worked point: (2, 2.414)
Orange point: horizontal coordinate 2, calculated vertical coordinate 2.4142. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2, into the displayed formula to obtain 2.4142 on the vertical axis. Values are rounded for display.

Scope. Positive yielded branch only; at zero rate the unyielded model allows a range of stresses.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Oldroyd-B model · Example 1

Polymer stress relaxation at rest

Problem & parameters. After a small deformation, hold the fluid motionless. A homogeneous Oldroyd-B polymer shear stress obeys λdτp/dt+τp=0.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Oldroyd-B model: Polymer stress relaxation at rest. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Polymer shear stress / initial stress (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Polymer shear stress / initial stress (dimensionless) Polymer stress relaxation at rest Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Zero-velocity, homogeneous stress-relaxation subproblem; convected terms vanish and the solvent stress is zero.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Reynolds-averaged Navier–Stokes (RANS) · Example 1

Laminar-limit flow verification

Problem & parameters. Verify the molecular-viscosity momentum equation using fully developed plane Poiseuille flow with turbulent or subgrid stresses disabled.

u/Umax⁡=1−ξ2u/U_{\max}=1-\xi^2

Solution. A constant pressure gradient gives μu″ = dp/dx. Apply no slip at the two walls and normalize by the center speed.

Reynolds-averaged Navier–Stokes (RANS): Laminar-limit flow verification. Horizontal axis: Position / channel half-width (dimensionless). Vertical axis: Velocity / center velocity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Position / channel half-width (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Velocity / center velocity (dimensionless) Laminar-limit flow verification Stated analytical example Worked point: (0, 1)
Orange point: horizontal coordinate 0, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Laminar-limit verification only; it neither models turbulence nor validates a RANS, LES, or DES closure.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Large-eddy simulation (LES) · Example 1

Laminar-limit flow verification

Problem & parameters. Verify the molecular-viscosity momentum equation using fully developed plane Poiseuille flow with turbulent or subgrid stresses disabled.

u/Umax⁡=1−ξ2u/U_{\max}=1-\xi^2

Solution. A constant pressure gradient gives μu″ = dp/dx. Apply no slip at the two walls and normalize by the center speed.

Large-eddy simulation (LES): Laminar-limit flow verification. Horizontal axis: Position / channel half-width (dimensionless). Vertical axis: Velocity / center velocity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Position / channel half-width (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Velocity / center velocity (dimensionless) Laminar-limit flow verification Stated analytical example Worked point: (0, 1)
Orange point: horizontal coordinate 0, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Laminar-limit verification only; it neither models turbulence nor validates a RANS, LES, or DES closure.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Detached-eddy simulation (DES) · Example 1

Laminar-limit flow verification

Problem & parameters. Verify the molecular-viscosity momentum equation using fully developed plane Poiseuille flow with turbulent or subgrid stresses disabled.

u/Umax⁡=1−ξ2u/U_{\max}=1-\xi^2

Solution. A constant pressure gradient gives μu″ = dp/dx. Apply no slip at the two walls and normalize by the center speed.

Detached-eddy simulation (DES): Laminar-limit flow verification. Horizontal axis: Position / channel half-width (dimensionless). Vertical axis: Velocity / center velocity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Position / channel half-width (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Velocity / center velocity (dimensionless) Laminar-limit flow verification Stated analytical example Worked point: (0, 1)
Orange point: horizontal coordinate 0, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Laminar-limit verification only; it neither models turbulence nor validates a RANS, LES, or DES closure.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Spalart–Allmaras model · Example 1

Spalart–Allmaras viscosity mapping

Problem & parameters. For nonnegative working variable χ evaluate the standard SA eddy-viscosity mapping with cv1 = 7.1.

νt/ν=χχ3χ3+7.13\nu_t/\nu=\chi\frac{\chi^3}{\chi^3+7.1^3}

Solution. Compute the damping function fv1 = χ³/(χ³+cv1³), then multiply by χ.

Spalart–Allmaras model: Spalart–Allmaras viscosity mapping. Horizontal axis: Working variable χ = ν̃ / ν (dimensionless). Vertical axis: Eddy viscosity νt / ν (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 Working variable χ = ν̃ / ν (dimensionless) 0 5 10 15 20 Eddy viscosity νt / ν (dimensionless) Spalart–Allmaras viscosity mapping Stated analytical example Worked point: (10, 7.364)
Orange point: horizontal coordinate 10, calculated vertical coordinate 7.3643. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 10, into the displayed formula to obtain 7.3643 on the vertical axis. Values are rounded for display.

Scope. Algebraic closure contribution only, not a solution of the SA transport equation.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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k–epsilon model · Example 1

k–epsilon eddy-viscosity closure

Problem & parameters. Hold dissipation ε = ε* fixed and use Cμ = 0.09.

νtϵ∗/k∗2=0.09(k/k∗)2\nu_t\epsilon_*/k_*^2=0.09(k/k_*)^2

Solution. Substitute k into νt = Cμk²/ε.

k–epsilon model: k–epsilon eddy-viscosity closure. Horizontal axis: Turbulent kinetic energy k / k* (dimensionless). Vertical axis: Scaled eddy viscosity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Turbulent kinetic energy k / k* (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Scaled eddy viscosity (dimensionless) k–epsilon eddy-viscosity closure Stated analytical example Worked point: (2, 0.36)
Orange point: horizontal coordinate 2, calculated vertical coordinate 0.36. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2, into the displayed formula to obtain 0.36 on the vertical axis. Values are rounded for display.

Scope. Closure evaluation, not a prediction of k or ε from their coupled transport equations.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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k–omega model · Example 1

k–omega viscosity closure

Problem & parameters. Hold specific dissipation ω = ω* > 0 and use the basic νt = k/ω relation.

νtω∗/k∗=k/k∗\nu_t\omega_*/k_* = k/k_*

Solution. Divide the closure by the reference viscosity k*/ω*.

k–omega model: k–omega viscosity closure. Horizontal axis: Turbulent kinetic energy k / k* (dimensionless). Vertical axis: Scaled eddy viscosity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Turbulent kinetic energy k / k* (dimensionless) 0 1 2 3 4 Scaled eddy viscosity (dimensionless) k–omega viscosity closure Stated analytical example Worked point: (2, 2)
Orange point: horizontal coordinate 2, calculated vertical coordinate 2. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2, into the displayed formula to obtain 2 on the vertical axis. Values are rounded for display.

Scope. Basic algebraic closure with fixed ω; model variants may include limiters.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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SST k–omega model · Example 1

SST shear-stress limiter

Problem & parameters. Hold positive k and ω fixed. Evaluate νt = a1k/max(a1ω,SF2) with a1 = 0.31.

νtω/k=0.31max⁡(0.31,s),s=SF2/ω\nu_t\omega/k=\frac{0.31}{\max(0.31,s)},\quad s=SF_2/\omega

Solution. Divide denominator and numerator by ω to expose the limiter transition at s = a1.

SST k–omega model: SST shear-stress limiter. Horizontal axis: Scaled strain SF₂ / ω (dimensionless). Vertical axis: Limited viscosity νtω / k (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 Scaled strain SF₂ / ω (dimensionless) 0.2 0.4 0.6 0.8 1.0 Limited viscosity νtω / k (dimensionless) SST shear-stress limiter Stated analytical example Worked point: (1, 0.31)
Orange point: horizontal coordinate 1, calculated vertical coordinate 0.31. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1, into the displayed formula to obtain 0.31 on the vertical axis. Values are rounded for display.

Scope. Algebraic SST limiter illustration; blending functions and transport equations are not solved.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Reynolds-stress transport model · Example 1

Idealized return to isotropy

Problem & parameters. For a homogeneous Reynolds-stress anisotropy component use the reduced closure db/dt = −b/T with constant T.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Reynolds-stress transport model: Idealized return to isotropy. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Anisotropy component / initial component (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Anisotropy component / initial component (dimensionless) Idealized return to isotropy Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Isolated linear return-to-isotropy term; production, transport, and changing dissipation are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Smagorinsky subgrid model · Example 1

Smagorinsky viscosity versus strain

Problem & parameters. Use Cs = 0.1 and constant filter width Δ.

νt/(Δ2S∗)=0.01(∣S∣/S∗)\nu_t/(\Delta^2 S_*)=0.01(|S|/S_*)

Solution. Evaluate νt = (CsΔ)²|S|.

Smagorinsky subgrid model: Smagorinsky viscosity versus strain. Horizontal axis: Resolved strain |S| / S* (dimensionless). Vertical axis: Scaled subgrid viscosity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Resolved strain |S| / S* (dimensionless) 0.00 0.01 0.02 0.03 0.04 0.05 Scaled subgrid viscosity (dimensionless) Smagorinsky viscosity versus strain Stated analytical example Worked point: (2.5, 0.025)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.025. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.025 on the vertical axis. Values are rounded for display.

Scope. Constant-coefficient closure; no dynamic procedure or wall damping is included.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Volume-of-fluid (VOF) representation · Example 1

A transported smooth volume fraction

Problem & parameters. Advect the initial smoothed interface α(x,0) = [1−tanh(5x)]/2 at unit velocity with no compression term.

α(x,1)=12[1−tanh⁡(5(x−1))]\alpha(x,1)=\tfrac12[1-\tanh(5(x-1))]

Solution. Characteristics give α(x,t) = α₀(x−t); evaluate t = 1.

Volume-of-fluid (VOF) representation: A transported smooth volume fraction. Horizontal axis: Position x (dimensionless). Vertical axis: Phase volume fraction α (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Position x (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Phase volume fraction α (dimensionless) A transported smooth volume fraction Stated analytical example Worked point: (1, 0.5)
Orange point: horizontal coordinate 1, calculated vertical coordinate 0.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1, into the displayed formula to obtain 0.5 on the vertical axis. Values are rounded for display.

Scope. Exact scalar-advection benchmark with a deliberately smooth interface; interface reconstruction and multiphase momentum are not solved.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Euler–Euler two-fluid model · Example 1

Two-phase slip relaxation

Problem & parameters. For two homogeneous phases coupled only by linear interphase drag, scale time by the combined drag relaxation time and slip by its initial value.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Euler–Euler two-fluid model: Two-phase slip relaxation. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Remaining fraction (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Remaining fraction (dimensionless) Two-phase slip relaxation Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Subtract the two phase momentum balances to obtain a decaying relative velocity; spatial transport, pressure gradients, and phase change are absent.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Lagrangian particle tracking · Example 1

Particle acceleration under Stokes drag

Problem & parameters. A particle starts at rest in a uniform fluid of constant speed U and experiences linear drag only.

v/U=1−e−t/τpv/U=1-e^{-t/\tau_p}

Solution. Solve τp v′ + v = U with v(0) = 0 using an integrating factor.

Lagrangian particle tracking: Particle acceleration under Stokes drag. Horizontal axis: Time / particle relaxation time (dimensionless). Vertical axis: Particle speed / fluid speed (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / particle relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Particle speed / fluid speed (dimensionless) Particle acceleration under Stokes drag Stated analytical example Worked point: (2.5, 0.9179)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.91792. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.91792 on the vertical axis. Values are rounded for display.

Scope. Dilute isolated-particle Stokes-drag reduction; no gravity or feedback on the fluid.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Fourier heat conduction · Example 1

Steady one-dimensional diffusion benchmark

Problem & parameters. Solve u″ = 0 on 0 < ξ < 1 with u(0) = 1 and u(1) = 0, constant transport coefficient, and no source.

u(ξ)=1−ξu(\xi)=1-\xi

Solution. Integrate twice to obtain u = A+Bξ. The two endpoint values give A = 1 and B = −1.

Fourier heat conduction: Steady one-dimensional diffusion benchmark. Horizontal axis: Position x / L (dimensionless). Vertical axis: Temperature excess / imposed difference (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Temperature excess / imposed difference (dimensionless) Steady one-dimensional diffusion benchmark Stated analytical example Worked point: (0.5, 0.5)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.5 on the vertical axis. Values are rounded for display.

Scope. For numerical-method entries this is the exact target to verify against, not a computed discretization or convergence claim.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Groundwater flow model · Example 1

Steady one-dimensional diffusion benchmark

Problem & parameters. Solve u″ = 0 on 0 < ξ < 1 with u(0) = 1 and u(1) = 0, constant transport coefficient, and no source.

u(ξ)=1−ξu(\xi)=1-\xi

Solution. Integrate twice to obtain u = A+Bξ. The two endpoint values give A = 1 and B = −1.

Groundwater flow model: Steady one-dimensional diffusion benchmark. Horizontal axis: Position x / L (dimensionless). Vertical axis: Hydraulic head excess / imposed difference (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Hydraulic head excess / imposed difference (dimensionless) Steady one-dimensional diffusion benchmark Stated analytical example Worked point: (0.5, 0.5)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.5 on the vertical axis. Values are rounded for display.

Scope. For numerical-method entries this is the exact target to verify against, not a computed discretization or convergence claim.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Finite element method (FEM / FEA) · Example 1

Steady one-dimensional diffusion benchmark

Problem & parameters. Solve u″ = 0 on 0 < ξ < 1 with u(0) = 1 and u(1) = 0, constant transport coefficient, and no source.

u(ξ)=1−ξu(\xi)=1-\xi

Solution. Integrate twice to obtain u = A+Bξ. The two endpoint values give A = 1 and B = −1.

Finite element method (FEM / FEA): Steady one-dimensional diffusion benchmark. Horizontal axis: Position x / L (dimensionless). Vertical axis: Normalized temperature or head (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Normalized temperature or head (dimensionless) Steady one-dimensional diffusion benchmark Stated analytical example Worked point: (0.5, 0.5)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.5 on the vertical axis. Values are rounded for display.

Scope. For numerical-method entries this is the exact target to verify against, not a computed discretization or convergence claim.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Finite volume method (FVM) · Example 1

Steady one-dimensional diffusion benchmark

Problem & parameters. Solve u″ = 0 on 0 < ξ < 1 with u(0) = 1 and u(1) = 0, constant transport coefficient, and no source.

u(ξ)=1−ξu(\xi)=1-\xi

Solution. Integrate twice to obtain u = A+Bξ. The two endpoint values give A = 1 and B = −1.

Finite volume method (FVM): Steady one-dimensional diffusion benchmark. Horizontal axis: Position x / L (dimensionless). Vertical axis: Normalized temperature or head (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Normalized temperature or head (dimensionless) Steady one-dimensional diffusion benchmark Stated analytical example Worked point: (0.5, 0.5)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.5 on the vertical axis. Values are rounded for display.

Scope. For numerical-method entries this is the exact target to verify against, not a computed discretization or convergence claim.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Finite difference method (FDM) · Example 1

Steady one-dimensional diffusion benchmark

Problem & parameters. Solve u″ = 0 on 0 < ξ < 1 with u(0) = 1 and u(1) = 0, constant transport coefficient, and no source.

u(ξ)=1−ξu(\xi)=1-\xi

Solution. Integrate twice to obtain u = A+Bξ. The two endpoint values give A = 1 and B = −1.

Finite difference method (FDM): Steady one-dimensional diffusion benchmark. Horizontal axis: Position x / L (dimensionless). Vertical axis: Normalized temperature or head (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Normalized temperature or head (dimensionless) Steady one-dimensional diffusion benchmark Stated analytical example Worked point: (0.5, 0.5)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.5 on the vertical axis. Values are rounded for display.

Scope. For numerical-method entries this is the exact target to verify against, not a computed discretization or convergence claim.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Boundary element method (BEM) · Example 1

Steady one-dimensional diffusion benchmark

Problem & parameters. Solve u″ = 0 on 0 < ξ < 1 with u(0) = 1 and u(1) = 0, constant transport coefficient, and no source.

u(ξ)=1−ξu(\xi)=1-\xi

Solution. Integrate twice to obtain u = A+Bξ. The two endpoint values give A = 1 and B = −1.

Boundary element method (BEM): Steady one-dimensional diffusion benchmark. Horizontal axis: Position x / L (dimensionless). Vertical axis: Normalized temperature or head (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Normalized temperature or head (dimensionless) Steady one-dimensional diffusion benchmark Stated analytical example Worked point: (0.5, 0.5)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.5 on the vertical axis. Values are rounded for display.

Scope. For numerical-method entries this is the exact target to verify against, not a computed discretization or convergence claim.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Transient heat equation · Example 1

Decaying heat-mode reference

Problem & parameters. Use uτ = uξξ on the unit interval, zero end values, and u(ξ,0) = sin(πξ). Plot τ = 0.1.

u(ξ,τ)=sin⁡(πξ)e−π2τ,τ=0.1u(\xi,\tau)=\sin(\pi\xi)e^{-\pi^2\tau},\quad\tau=0.1

Solution. The sine satisfies both zero end values. Its second derivative is −π² times itself; the amplitude solves a′ = −π²a.

Transient heat equation: Decaying heat-mode reference. Horizontal axis: Position x / L (dimensionless). Vertical axis: Temperature perturbation / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.1 0.2 0.3 0.4 Temperature perturbation / initial amplitude (dimensionless) Decaying heat-mode reference Stated analytical example Worked point: (0.5, 0.3727)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.37271. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.37271 on the vertical axis. Values are rounded for display.

Scope. Exact PDE benchmark. For reduced bases, PINNs, and neural operators, this is a reference target, not a claimed trained or computed prediction.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Spectral method · Example 1

Decaying heat-mode reference

Problem & parameters. Use uτ = uξξ on the unit interval, zero end values, and u(ξ,0) = sin(πξ). Plot τ = 0.1.

u(ξ,τ)=sin⁡(πξ)e−π2τ,τ=0.1u(\xi,\tau)=\sin(\pi\xi)e^{-\pi^2\tau},\quad\tau=0.1

Solution. The sine satisfies both zero end values. Its second derivative is −π² times itself; the amplitude solves a′ = −π²a.

Spectral method: Decaying heat-mode reference. Horizontal axis: Position x / L (dimensionless). Vertical axis: Field / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.1 0.2 0.3 0.4 Field / initial amplitude (dimensionless) Decaying heat-mode reference Stated analytical example Worked point: (0.5, 0.3727)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.37271. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.37271 on the vertical axis. Values are rounded for display.

Scope. Exact PDE benchmark. For reduced bases, PINNs, and neural operators, this is a reference target, not a claimed trained or computed prediction.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Reduced basis model · Example 1

Decaying heat-mode reference

Problem & parameters. Use uτ = uξξ on the unit interval, zero end values, and u(ξ,0) = sin(πξ). Plot τ = 0.1.

u(ξ,τ)=sin⁡(πξ)e−π2τ,τ=0.1u(\xi,\tau)=\sin(\pi\xi)e^{-\pi^2\tau},\quad\tau=0.1

Solution. The sine satisfies both zero end values. Its second derivative is −π² times itself; the amplitude solves a′ = −π²a.

Reduced basis model: Decaying heat-mode reference. Horizontal axis: Position x / L (dimensionless). Vertical axis: Field / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.1 0.2 0.3 0.4 Field / initial amplitude (dimensionless) Decaying heat-mode reference Stated analytical example Worked point: (0.5, 0.3727)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.37271. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.37271 on the vertical axis. Values are rounded for display.

Scope. Exact PDE benchmark. For reduced bases, PINNs, and neural operators, this is a reference target, not a claimed trained or computed prediction.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Physics-informed neural network (PINN) · Example 1

Decaying heat-mode reference

Problem & parameters. Use uτ = uξξ on the unit interval, zero end values, and u(ξ,0) = sin(πξ). Plot τ = 0.1.

u(ξ,τ)=sin⁡(πξ)e−π2τ,τ=0.1u(\xi,\tau)=\sin(\pi\xi)e^{-\pi^2\tau},\quad\tau=0.1

Solution. The sine satisfies both zero end values. Its second derivative is −π² times itself; the amplitude solves a′ = −π²a.

Physics-informed neural network (PINN): Decaying heat-mode reference. Horizontal axis: Position x / L (dimensionless). Vertical axis: Field / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.1 0.2 0.3 0.4 Field / initial amplitude (dimensionless) Decaying heat-mode reference Stated analytical example Worked point: (0.5, 0.3727)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.37271. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.37271 on the vertical axis. Values are rounded for display.

Scope. Exact PDE benchmark. For reduced bases, PINNs, and neural operators, this is a reference target, not a claimed trained or computed prediction.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Neural operator · Example 1

Decaying heat-mode reference

Problem & parameters. Use uτ = uξξ on the unit interval, zero end values, and u(ξ,0) = sin(πξ). Plot τ = 0.1.

u(ξ,τ)=sin⁡(πξ)e−π2τ,τ=0.1u(\xi,\tau)=\sin(\pi\xi)e^{-\pi^2\tau},\quad\tau=0.1

Solution. The sine satisfies both zero end values. Its second derivative is −π² times itself; the amplitude solves a′ = −π²a.

Neural operator: Decaying heat-mode reference. Horizontal axis: Position x / L (dimensionless). Vertical axis: Field / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.1 0.2 0.3 0.4 Field / initial amplitude (dimensionless) Decaying heat-mode reference Stated analytical example Worked point: (0.5, 0.3727)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.37271. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.37271 on the vertical axis. Values are rounded for display.

Scope. Exact PDE benchmark. For reduced bases, PINNs, and neural operators, this is a reference target, not a claimed trained or computed prediction.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Lumped-capacitance thermal model · Example 1

Single thermal capacitance cooling

Problem & parameters. A thermal capacitance C connects through resistance R to fixed ambient temperature. Set τ = t/(RC) and y = (T−T∞)/(T0−T∞).

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Lumped-capacitance thermal model: Single thermal capacitance cooling. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Temperature excess / initial excess (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Temperature excess / initial excess (dimensionless) Single thermal capacitance cooling Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. One-node constant-property cooling example; multizone and multi-node networks have additional modes.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Thermal resistance-capacitance network · Example 1

Single thermal capacitance cooling

Problem & parameters. A thermal capacitance C connects through resistance R to fixed ambient temperature. Set τ = t/(RC) and y = (T−T∞)/(T0−T∞).

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Thermal resistance-capacitance network: Single thermal capacitance cooling. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Temperature excess / initial excess (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Temperature excess / initial excess (dimensionless) Single thermal capacitance cooling Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. One-node constant-property cooling example; multizone and multi-node networks have additional modes.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Newton cooling model · Example 1

Single thermal capacitance cooling

Problem & parameters. A thermal capacitance C connects through resistance R to fixed ambient temperature. Set τ = t/(RC) and y = (T−T∞)/(T0−T∞).

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Newton cooling model: Single thermal capacitance cooling. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Temperature excess / initial excess (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Temperature excess / initial excess (dimensionless) Single thermal capacitance cooling Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. One-node constant-property cooling example; multizone and multi-node networks have additional modes.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Building thermal-zone model · Example 1

Single thermal capacitance cooling

Problem & parameters. A thermal capacitance C connects through resistance R to fixed ambient temperature. Set τ = t/(RC) and y = (T−T∞)/(T0−T∞).

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Building thermal-zone model: Single thermal capacitance cooling. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Temperature excess / initial excess (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Temperature excess / initial excess (dimensionless) Single thermal capacitance cooling Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. One-node constant-property cooling example; multizone and multi-node networks have additional modes.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Radiative transfer equation · Example 1

Uncollided beam attenuation

Problem & parameters. A steady beam traverses a homogeneous purely absorbing medium. Set τ = Σx and y = intensity / incident intensity.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Radiative transfer equation: Uncollided beam attenuation. Horizontal axis: Optical thickness Σx (dimensionless). Vertical axis: Beam intensity / incident intensity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Optical thickness Σx (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Beam intensity / incident intensity (dimensionless) Uncollided beam attenuation Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Exact absorption-only transport benchmark, without scattering or emission. For Monte Carlo transport this is the expected value, not a sampled realization.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Neutron transport model · Example 1

Uncollided beam attenuation

Problem & parameters. A steady beam traverses a homogeneous purely absorbing medium. Set τ = Σx and y = intensity / incident intensity.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Neutron transport model: Uncollided beam attenuation. Horizontal axis: Optical thickness Σx (dimensionless). Vertical axis: Beam intensity / incident intensity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Optical thickness Σx (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Beam intensity / incident intensity (dimensionless) Uncollided beam attenuation Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Exact absorption-only transport benchmark, without scattering or emission. For Monte Carlo transport this is the expected value, not a sampled realization.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Monte Carlo transport · Example 1

Uncollided beam attenuation

Problem & parameters. A steady beam traverses a homogeneous purely absorbing medium. Set τ = Σx and y = intensity / incident intensity.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Monte Carlo transport: Uncollided beam attenuation. Horizontal axis: Optical thickness Σx (dimensionless). Vertical axis: Beam intensity / incident intensity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Optical thickness Σx (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Beam intensity / incident intensity (dimensionless) Uncollided beam attenuation Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Exact absorption-only transport benchmark, without scattering or emission. For Monte Carlo transport this is the expected value, not a sampled realization.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Stefan–Boltzmann surface model · Example 1

Net radiation to a fixed surrounding

Problem & parameters. A gray surface sees a large isothermal surrounding at T*, with constant emissivity.

q/(ϵσT∗4)=θ4−1q/(\epsilon\sigma T_*^4)=\theta^4-1

Solution. Subtract incoming εσT*⁴ from outgoing εσT⁴. Positive net flux is outward.

Stefan–Boltzmann surface model: Net radiation to a fixed surrounding. Horizontal axis: Surface / surrounding temperature T / T* (dimensionless). Vertical axis: Scaled net radiative flux (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 Surface / surrounding temperature T / T* (dimensionless) −2.5 0.0 2.5 5.0 7.5 10.0 12.5 15.0 Scaled net radiative flux (dimensionless) Net radiation to a fixed surrounding Stated analytical example Worked point: (1.25, 1.441)
Orange point: horizontal coordinate 1.25, calculated vertical coordinate 1.4414. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.25, into the displayed formula to obtain 1.4414 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Surface-to-surface radiosity model · Example 1

Two gray parallel surfaces

Problem & parameters. Two infinite parallel diffuse-gray plates have emissivities ε and 0.8 and fixed unequal temperatures.

qσ(T14−T24)=11/ϵ+1/0.8−1\frac{q}{\sigma(T_1^4-T_2^4)}=\frac1{1/\epsilon+1/0.8-1}

Solution. Add the two surface radiation resistances and the unit view-factor space resistance; solve the radiosity balance for q.

Surface-to-surface radiosity model: Two gray parallel surfaces. Horizontal axis: Surface 1 emissivity ε (dimensionless). Vertical axis: Radiative exchange factor (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.2 0.4 0.6 0.8 1.0 Surface 1 emissivity ε (dimensionless) 0.0 0.2 0.4 0.6 0.8 Radiative exchange factor (dimensionless) Two gray parallel surfaces Stated analytical example Worked point: (0.525, 0.4641)
Orange point: horizontal coordinate 0.525, calculated vertical coordinate 0.46409. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.525, into the displayed formula to obtain 0.46409 on the vertical axis. Values are rounded for display.

Scope. Equal facing areas, view factor one, and a nonparticipating gap.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Stefan phase-change problem · Example 1

Similarity-law melt-front position

Problem & parameters. Take a one-phase Stefan problem whose Stefan number selects similarity constant λ = 0.5.

s/L=2λαt/L2,λ=0.5s/L=2\lambda\sqrt{\alpha t/L^2},\quad\lambda=0.5

Solution. The diffusion similarity coordinate makes the interface position s = 2λ√(αt). For this λ, the Stefan-number relation is Ste = √π λ exp(λ²) erf(λ) ≈ 0.5923.

Stefan phase-change problem: Similarity-law melt-front position. Horizontal axis: Fourier time αt / L² (dimensionless). Vertical axis: Front position s / L (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Fourier time αt / L² (dimensionless) 0.0 0.5 1.0 1.5 2.0 Front position s / L (dimensionless) Similarity-law melt-front position Stated analytical example Worked point: (2, 1.414)
Orange point: horizontal coordinate 2, calculated vertical coordinate 1.4142. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2, into the displayed formula to obtain 1.4142 on the vertical axis. Values are rounded for display.

Scope. Semi-infinite, one-phase conduction limit with a fixed boundary temperature; λ must be consistent with the material and thermal data.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Enthalpy–porosity model · Example 1

Prescribed mushy-range liquid fraction

Problem & parameters. Choose the linear liquid-fraction law between solidus Ts and liquidus Tl.

fl=min⁡[1,max⁡(0,θ)],θ=(T−Ts)/(Tl−Ts)f_l=\min[1,\max(0,\theta)],\quad\theta=(T-T_s)/(T_l-T_s)

Solution. Use zero fraction below Ts, linear interpolation inside the mushy interval, and unit fraction above Tl.

Enthalpy–porosity model: Prescribed mushy-range liquid fraction. Horizontal axis: Temperature within melting interval θ (dimensionless). Vertical axis: Liquid fraction (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 1.25 1.50 Temperature within melting interval θ (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Liquid fraction (dimensionless) Prescribed mushy-range liquid fraction Stated analytical example Worked point: (0.5, 0.5)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.5 on the vertical axis. Values are rounded for display.

Scope. Constitutive phase-fraction example only; momentum damping and the transient enthalpy equation are not solved.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Linear elasticity (Hooke model) · Example 1

Uniform axial extension

Problem & parameters. Apply uniform uniaxial strain to a homogeneous small-strain elastic bar with traction-free lateral surfaces.

σ/E=ε\sigma/E=\varepsilon

Solution. The one-dimensional constitutive law is σ = Eε; divide by E. For an orthotropic solid use its modulus along a principal material axis.

Linear elasticity (Hooke model): Uniform axial extension. Horizontal axis: Axial strain ε (dimensionless). Vertical axis: Axial stress / directional modulus (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.000 0.002 0.004 0.006 0.008 0.010 Axial strain ε (dimensionless) 0.000 0.002 0.004 0.006 0.008 0.010 Axial stress / directional modulus (dimensionless) Uniform axial extension Stated analytical example Worked point: (0.005, 0.005)
Orange point: horizontal coordinate 0.005, calculated vertical coordinate 0.005. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.005, into the displayed formula to obtain 0.005 on the vertical axis. Values are rounded for display.

Scope. Homogeneous linear reference for truss, RVE, and FE² entries; this is not a heterogeneous microscale simulation.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Orthotropic elasticity · Example 1

Uniform axial extension

Problem & parameters. Apply uniform uniaxial strain to a homogeneous small-strain elastic bar with traction-free lateral surfaces.

σ/E=ε\sigma/E=\varepsilon

Solution. The one-dimensional constitutive law is σ = Eε; divide by E. For an orthotropic solid use its modulus along a principal material axis.

Orthotropic elasticity: Uniform axial extension. Horizontal axis: Axial strain ε (dimensionless). Vertical axis: Axial stress / directional modulus (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.000 0.002 0.004 0.006 0.008 0.010 Axial strain ε (dimensionless) 0.000 0.002 0.004 0.006 0.008 0.010 Axial stress / directional modulus (dimensionless) Uniform axial extension Stated analytical example Worked point: (0.005, 0.005)
Orange point: horizontal coordinate 0.005, calculated vertical coordinate 0.005. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.005, into the displayed formula to obtain 0.005 on the vertical axis. Values are rounded for display.

Scope. Homogeneous linear reference for truss, RVE, and FE² entries; this is not a heterogeneous microscale simulation.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Truss model · Example 1

Uniform axial extension

Problem & parameters. Apply uniform uniaxial strain to a homogeneous small-strain elastic bar with traction-free lateral surfaces.

σ/E=ε\sigma/E=\varepsilon

Solution. The one-dimensional constitutive law is σ = Eε; divide by E. For an orthotropic solid use its modulus along a principal material axis.

Truss model: Uniform axial extension. Horizontal axis: Axial strain ε (dimensionless). Vertical axis: Axial stress / directional modulus (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.000 0.002 0.004 0.006 0.008 0.010 Axial strain ε (dimensionless) 0.000 0.002 0.004 0.006 0.008 0.010 Axial stress / directional modulus (dimensionless) Uniform axial extension Stated analytical example Worked point: (0.005, 0.005)
Orange point: horizontal coordinate 0.005, calculated vertical coordinate 0.005. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.005, into the displayed formula to obtain 0.005 on the vertical axis. Values are rounded for display.

Scope. Homogeneous linear reference for truss, RVE, and FE² entries; this is not a heterogeneous microscale simulation.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Representative volume element (RVE) · Example 1

Uniform axial extension

Problem & parameters. Apply uniform uniaxial strain to a homogeneous small-strain elastic bar with traction-free lateral surfaces.

σ/E=ε\sigma/E=\varepsilon

Solution. The one-dimensional constitutive law is σ = Eε; divide by E. For an orthotropic solid use its modulus along a principal material axis.

Representative volume element (RVE): Uniform axial extension. Horizontal axis: Axial strain ε (dimensionless). Vertical axis: Axial stress / directional modulus (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.000 0.002 0.004 0.006 0.008 0.010 Axial strain ε (dimensionless) 0.000 0.002 0.004 0.006 0.008 0.010 Axial stress / directional modulus (dimensionless) Uniform axial extension Stated analytical example Worked point: (0.005, 0.005)
Orange point: horizontal coordinate 0.005, calculated vertical coordinate 0.005. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.005, into the displayed formula to obtain 0.005 on the vertical axis. Values are rounded for display.

Scope. Homogeneous linear reference for truss, RVE, and FE² entries; this is not a heterogeneous microscale simulation.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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FE² computational homogenization · Example 1

Uniform axial extension

Problem & parameters. Apply uniform uniaxial strain to a homogeneous small-strain elastic bar with traction-free lateral surfaces.

σ/E=ε\sigma/E=\varepsilon

Solution. The one-dimensional constitutive law is σ = Eε; divide by E. For an orthotropic solid use its modulus along a principal material axis.

FE² computational homogenization: Uniform axial extension. Horizontal axis: Axial strain ε (dimensionless). Vertical axis: Axial stress / directional modulus (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.000 0.002 0.004 0.006 0.008 0.010 Axial strain ε (dimensionless) 0.000 0.002 0.004 0.006 0.008 0.010 Axial stress / directional modulus (dimensionless) Uniform axial extension Stated analytical example Worked point: (0.005, 0.005)
Orange point: horizontal coordinate 0.005, calculated vertical coordinate 0.005. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.005, into the displayed formula to obtain 0.005 on the vertical axis. Values are rounded for display.

Scope. Homogeneous linear reference for truss, RVE, and FE² entries; this is not a heterogeneous microscale simulation.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Neo-Hookean hyperelasticity · Example 1

Incompressible neo-Hookean tension

Problem & parameters. Stretch an incompressible neo-Hookean solid uniaxially with traction-free transverse faces.

σ/μ=λ2−λ−1\sigma/\mu=\lambda^2-\lambda^{-1}

Solution. Incompressibility gives transverse stretches λ^−1/2. Eliminate the pressure using zero transverse stress, yielding μ(λ²−λ^−1).

Neo-Hookean hyperelasticity: Incompressible neo-Hookean tension. Horizontal axis: Axial stretch λ (dimensionless). Vertical axis: Cauchy stress / shear modulus (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 Axial stretch λ (dimensionless) −1 0 1 2 3 4 Cauchy stress / shear modulus (dimensionless) Incompressible neo-Hookean tension Stated analytical example Worked point: (1.3, 0.9208)
Orange point: horizontal coordinate 1.3, calculated vertical coordinate 0.92077. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.3, into the displayed formula to obtain 0.92077 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Mooney–Rivlin hyperelasticity · Example 1

Mooney–Rivlin uniaxial tension

Problem & parameters. Use an incompressible two-parameter Mooney–Rivlin material with C10 = C01 and μ = 2(C10+C01).

σ/μ=12(1+λ−1)(λ2−λ−1)\sigma/\mu=\tfrac12(1+\lambda^{-1})(\lambda^2-\lambda^{-1})

Solution. Differentiate the strain energy and eliminate transverse pressure. The axial stress is 2(C10+C01/λ)(λ²−1/λ).

Mooney–Rivlin hyperelasticity: Mooney–Rivlin uniaxial tension. Horizontal axis: Axial stretch λ (dimensionless). Vertical axis: Cauchy stress / initial shear modulus (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 Axial stretch λ (dimensionless) −2 −1 0 1 2 3 Cauchy stress / initial shear modulus (dimensionless) Mooney–Rivlin uniaxial tension Stated analytical example Worked point: (1.3, 0.8145)
Orange point: horizontal coordinate 1.3, calculated vertical coordinate 0.81453. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.3, into the displayed formula to obtain 0.81453 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Ogden hyperelasticity · Example 1

One-term Ogden tension

Problem & parameters. Choose W = (2μ/α²)(λ1^α+λ2^α+λ3^α−3), α = 4, and incompressible uniaxial tension.

σ/μ=12(λ4−λ−2)\sigma/\mu=\tfrac12(\lambda^4-\lambda^{-2})

Solution. Set transverse stretches to λ^−1/2 and impose zero transverse stress. Then σ = (2μ/α)(λ^α−λ^−α/2).

Ogden hyperelasticity: One-term Ogden tension. Horizontal axis: Axial stretch λ (dimensionless). Vertical axis: Cauchy stress / initial shear modulus (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 Axial stretch λ (dimensionless) −2 0 2 4 6 8 Cauchy stress / initial shear modulus (dimensionless) One-term Ogden tension Stated analytical example Worked point: (1.3, 1.132)
Orange point: horizontal coordinate 1.3, calculated vertical coordinate 1.1322. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.3, into the displayed formula to obtain 1.1322 on the vertical axis. Values are rounded for display.

Scope. The energy convention is stated explicitly because Ogden coefficient conventions vary.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Euler–Bernoulli beam model · Example 1

End-loaded cantilever deflection

Problem & parameters. A prismatic Euler–Bernoulli cantilever of length L carries a transverse tip force P.

w/(PL3/EI)=ξ2(3−ξ)/6w/(PL^3/EI)=\xi^2(3-\xi)/6

Solution. Use bending moment M = P(L−x). Integrate EIw″ = M and apply zero displacement and slope at the clamped end.

Euler–Bernoulli beam model: End-loaded cantilever deflection. Horizontal axis: Axial position ξ = x / L (dimensionless). Vertical axis: Deflection w / (PL³/EI) (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Axial position ξ = x / L (dimensionless) 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 Deflection w / (PL³/EI) (dimensionless) End-loaded cantilever deflection Stated analytical example Worked point: (0.5, 0.1042)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.10417. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.10417 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Timoshenko beam model · Example 1

Cantilever bending plus shear

Problem & parameters. Take an end-loaded Timoshenko cantilever with EI/(κGA L²) = 0.1.

w/(PL3/EI)=ξ2(3−ξ)/6+0.1ξw/(PL^3/EI)=\xi^2(3-\xi)/6+0.1\xi

Solution. Add the bending displacement to the shear contribution Px/(κGA). Divide by PL³/EI.

Timoshenko beam model: Cantilever bending plus shear. Horizontal axis: Axial position x / L (dimensionless). Vertical axis: Scaled transverse deflection (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Axial position x / L (dimensionless) 0.0 0.1 0.2 0.3 0.4 Scaled transverse deflection (dimensionless) Cantilever bending plus shear Stated analytical example Worked point: (0.5, 0.1542)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.15417. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.15417 on the vertical axis. Values are rounded for display.

Scope. Linear prismatic beam; κ is the shear correction factor.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Kirchhoff–Love plate model · Example 1

Sinusoidally loaded plate centerline

Problem & parameters. Apply a single sinusoidal load mode to a simply supported rectangular plate. Plot its normalized centerline deflection.

w(x,b/2)/wmax⁡=sin⁡(πx/a)w(x,b/2)/w_{\max}=\sin(\pi x/a)

Solution. A separable sin(πx/a)sin(πy/b) mode satisfies the simply supported displacement conditions. At y=b/2 it reduces to the displayed sine.

Kirchhoff–Love plate model: Sinusoidally loaded plate centerline. Horizontal axis: Plate position x / a (dimensionless). Vertical axis: Centerline deflection / maximum (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Plate position x / a (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Centerline deflection / maximum (dimensionless) Sinusoidally loaded plate centerline Stated analytical example Worked point: (0.5, 1)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Kirchhoff–Love and compatible Mindlin single-mode solutions share this normalized shape but have different bending/shear amplitude formulas.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Mindlin–Reissner plate model · Example 1

Sinusoidally loaded plate centerline

Problem & parameters. Apply a single sinusoidal load mode to a simply supported rectangular plate. Plot its normalized centerline deflection.

w(x,b/2)/wmax⁡=sin⁡(πx/a)w(x,b/2)/w_{\max}=\sin(\pi x/a)

Solution. A separable sin(πx/a)sin(πy/b) mode satisfies the simply supported displacement conditions. At y=b/2 it reduces to the displayed sine.

Mindlin–Reissner plate model: Sinusoidally loaded plate centerline. Horizontal axis: Plate position x / a (dimensionless). Vertical axis: Centerline deflection / maximum (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Plate position x / a (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Centerline deflection / maximum (dimensionless) Sinusoidally loaded plate centerline Stated analytical example Worked point: (0.5, 1)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Kirchhoff–Love and compatible Mindlin single-mode solutions share this normalized shape but have different bending/shear amplitude formulas.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Shell model · Example 1

Thin spherical shell membrane stress

Problem & parameters. A thin spherical shell of radius R and thickness t carries uniform internal pressure p.

σ/(E)=12 [pR/(Et)]\sigma/(E)=\tfrac12\,[pR/(Et)]

Solution. Balance pressure on a hemisphere against the circumferential membrane force: pπR² = 2πRtσ.

Shell model: Thin spherical shell membrane stress. Horizontal axis: Pressure loading pR / Et (dimensionless). Vertical axis: Membrane stress σ / E (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0000 0.0025 0.0050 0.0075 0.0100 0.0125 0.0150 0.0175 0.0200 Pressure loading pR / Et (dimensionless) 0.000 0.002 0.004 0.006 0.008 0.010 Membrane stress σ / E (dimensionless) Thin spherical shell membrane stress Stated analytical example Worked point: (0.01, 0.005)
Orange point: horizontal coordinate 0.01, calculated vertical coordinate 0.005. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.01, into the displayed formula to obtain 0.005 on the vertical axis. Values are rounded for display.

Scope. Thin-shell membrane approximation, away from supports and local bending disturbances.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Cable and membrane models · Example 1

Hanging cable shape

Problem & parameters. An ideal flexible cable supports its own uniform weight per arc length; choose a = horizontal tension / weight per length.

y/a=cosh⁡(x/a)−1y/a=\cosh(x/a)-1

Solution. Force balance gives y″ = √(1+y′²)/a. Symmetry at the lowest point integrates to the catenary.

Cable and membrane models: Hanging cable shape. Horizontal axis: Horizontal distance x / a (dimensionless). Vertical axis: Height above lowest point y / a (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 Horizontal distance x / a (dimensionless) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Height above lowest point y / a (dimensionless) Hanging cable shape Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Self-weight catenary, not the parabolic approximation for uniform load per horizontal span.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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von Mises J2 plasticity · Example 1

Ideal uniaxial elastic-perfectly-plastic response

Problem & parameters. Load monotonically in uniaxial tension from an unstressed state, with no hardening.

σ/σy=min⁡(Eε/σy,1)\sigma/\sigma_y=\min(E\varepsilon/\sigma_y,1)

Solution. Use Hooke’s law until σ = σy. Further strain is plastic while stress stays at σy.

von Mises J2 plasticity: Ideal uniaxial elastic-perfectly-plastic response. Horizontal axis: Total strain Eε / σy (dimensionless). Vertical axis: Axial stress / yield stress (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Total strain Eε / σy (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Axial stress / yield stress (dimensionless) Ideal uniaxial elastic-perfectly-plastic response Stated analytical example Worked point: (1.5, 1)
Orange point: horizontal coordinate 1.5, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Uniaxial case where J2 and Tresca coincide; multiaxial yield surfaces differ.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Tresca yield model · Example 1

Ideal uniaxial elastic-perfectly-plastic response

Problem & parameters. Load monotonically in uniaxial tension from an unstressed state, with no hardening.

σ/σy=min⁡(Eε/σy,1)\sigma/\sigma_y=\min(E\varepsilon/\sigma_y,1)

Solution. Use Hooke’s law until σ = σy. Further strain is plastic while stress stays at σy.

Tresca yield model: Ideal uniaxial elastic-perfectly-plastic response. Horizontal axis: Total strain Eε / σy (dimensionless). Vertical axis: Axial stress / yield stress (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Total strain Eε / σy (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Axial stress / yield stress (dimensionless) Ideal uniaxial elastic-perfectly-plastic response Stated analytical example Worked point: (1.5, 1)
Orange point: horizontal coordinate 1.5, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Uniaxial case where J2 and Tresca coincide; multiaxial yield surfaces differ.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Drucker–Prager plasticity · Example 1

Pressure-dependent Drucker–Prager strength

Problem & parameters. Define the illustrative yield line q−0.5p−c=0 with compression-positive pressure p.

q/c=1+0.5(p/c)q/c=1+0.5(p/c)

Solution. Solve the stated yield function for q.

Drucker–Prager plasticity: Pressure-dependent Drucker–Prager strength. Horizontal axis: Compressive mean stress p / c (dimensionless). Vertical axis: Deviatoric strength q / c (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Compressive mean stress p / c (dimensionless) 1.0 1.5 2.0 2.5 3.0 Deviatoric strength q / c (dimensionless) Pressure-dependent Drucker–Prager strength Stated analytical example Worked point: (2, 2)
Orange point: horizontal coordinate 2, calculated vertical coordinate 2. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2, into the displayed formula to obtain 2 on the vertical axis. Values are rounded for display.

Scope. A specified pressure/deviatoric convention and slope; different parameter mappings to friction angle exist.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Mohr–Coulomb model · Example 1

Mohr–Coulomb shear strength

Problem & parameters. Use cohesion c > 0 and friction angle 30 degrees.

τf/c=1+(σn/c)tan⁡(30∘)\tau_f/c=1+(\sigma_n/c)\tan(30^\circ)

Solution. Insert the normal stress into τf = c+σn tan φ with compression positive.

Mohr–Coulomb model: Mohr–Coulomb shear strength. Horizontal axis: Compressive normal stress σn / c (dimensionless). Vertical axis: Shear strength τf / c (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Compressive normal stress σn / c (dimensionless) 1.0 1.5 2.0 2.5 3.0 3.5 Shear strength τf / c (dimensionless) Mohr–Coulomb shear strength Stated analytical example Worked point: (2, 2.155)
Orange point: horizontal coordinate 2, calculated vertical coordinate 2.1547. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2, into the displayed formula to obtain 2.1547 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Johnson–Cook model · Example 1

Johnson–Cook strain-hardening factor

Problem & parameters. Set B/A = 0.5, n = 0.5, strain rate equal to its reference value, and homologous temperature zero.

σ/A=1+0.5εp\sigma/A=1+0.5\sqrt{\varepsilon_p}

Solution. The rate and thermal factors become one. Evaluate the remaining A+Bεpⁿ hardening term.

Johnson–Cook model: Johnson–Cook strain-hardening factor. Horizontal axis: Equivalent plastic strain (dimensionless). Vertical axis: Flow stress / A (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Equivalent plastic strain (dimensionless) 1.0 1.1 1.2 1.3 1.4 1.5 Flow stress / A (dimensionless) Johnson–Cook strain-hardening factor Stated analytical example Worked point: (0.5, 1.354)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1.3536. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1.3536 on the vertical axis. Values are rounded for display.

Scope. Illustrative constants, not a calibrated metal response.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Crystal plasticity · Example 1

One slip-system power law

Problem & parameters. For positive resolved shear choose rate sensitivity m = 0.2 and fixed slip resistance g.

γ˙/γ˙0=(τ/g)5\dot\gamma/\dot\gamma_0=(\tau/g)^5

Solution. Evaluate γ̇ = γ̇0(τ/g)^(1/m).

Crystal plasticity: One slip-system power law. Horizontal axis: Resolved shear / slip resistance τ/g (dimensionless). Vertical axis: Slip rate / reference rate (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Resolved shear / slip resistance τ/g (dimensionless) 0 2 4 6 8 Slip rate / reference rate (dimensionless) One slip-system power law Stated analytical example Worked point: (0.75, 0.2373)
Orange point: horizontal coordinate 0.75, calculated vertical coordinate 0.2373. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.75, into the displayed formula to obtain 0.2373 on the vertical axis. Values are rounded for display.

Scope. Single-system constitutive evaluation; lattice rotation and hardening are held fixed.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Maxwell viscoelastic model · Example 1

Maxwell stress relaxation

Problem & parameters. Apply a step strain ε₀ to a Maxwell spring–dashpot series element and hold it fixed. Scale stress by Eε₀ and time by η/E.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Maxwell viscoelastic model: Maxwell stress relaxation. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Stress / initial stress (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Stress / initial stress (dimensionless) Maxwell stress relaxation Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Exact one-mode reduction with constant coefficients; additional coupled physics is excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Kelvin–Voigt model · Example 1

Kelvin–Voigt creep after a stress step

Problem & parameters. Apply constant stress σ₀ at t = 0 to an initially undeformed parallel spring and dashpot.

Eε/σ0=1−e−Et/ηE\varepsilon/\sigma_0=1-e^{-Et/\eta}

Solution. Solve ηε′+Eε = σ₀ with zero initial strain.

Kelvin–Voigt model: Kelvin–Voigt creep after a stress step. Horizontal axis: Time Et / η (dimensionless). Vertical axis: Normalized creep strain Eε / σ₀ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time Et / η (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Normalized creep strain Eε / σ₀ (dimensionless) Kelvin–Voigt creep after a stress step Stated analytical example Worked point: (2.5, 0.9179)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.91792. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.91792 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Standard linear solid · Example 1

Standard-linear-solid relaxation

Problem & parameters. Apply a fixed strain step to a standard linear solid with relaxed modulus E∞ = 0.4E0.

σ/(E0ε0)=0.4+0.6e−t/τ\sigma/(E_0\varepsilon_0)=0.4+0.6e^{-t/\tau}

Solution. Its relaxation modulus is E∞+(E0−E∞)exp(−t/τ). Multiply by the imposed strain.

Standard linear solid: Standard-linear-solid relaxation. Horizontal axis: Time t / τ (dimensionless). Vertical axis: Stress / initial stress (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time t / τ (dimensionless) 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Stress / initial stress (dimensionless) Standard-linear-solid relaxation Stated analytical example Worked point: (2.5, 0.4493)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.44925. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.44925 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Norton creep law · Example 1

Norton creep-rate sensitivity

Problem & parameters. At fixed temperature use Norton exponent n = 3 and reference rate Aσ*³.

ε˙/ε˙∗=(σ/σ∗)3\dot\varepsilon/\dot\varepsilon_*=(\sigma/\sigma_*)^3

Solution. Substitute the stress into ε̇ = Aσ³.

Norton creep law: Norton creep-rate sensitivity. Horizontal axis: Stress σ / σ* (dimensionless). Vertical axis: Creep rate / reference rate (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 Stress σ / σ* (dimensionless) 0 2 4 6 8 Creep rate / reference rate (dimensionless) Norton creep-rate sensitivity Stated analytical example Worked point: (1, 1)
Orange point: horizontal coordinate 1, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Steady creep constitutive law at fixed material state and temperature.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Linear elastic fracture mechanics (LEFM) · Example 1

Crack-tip opening stress on the forward ray

Problem & parameters. Use the leading mode-I elastic crack-tip field on θ = 0.

σyy/(KI/2πℓ)=(r/ℓ)−1/2\sigma_{yy}/(K_I/\sqrt{2\pi\ell})=(r/\ell)^{-1/2}

Solution. The angular factor equals one on the forward ray. Evaluate KI/√(2πr).

Linear elastic fracture mechanics (LEFM): Crack-tip opening stress on the forward ray. Horizontal axis: Distance ahead of tip r / ℓ (dimensionless). Vertical axis: Scaled opening stress (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 Distance ahead of tip r / ℓ (dimensionless) 1 2 3 4 Scaled opening stress (dimensionless) Crack-tip opening stress on the forward ray Stated analytical example Worked point: (1.025, 0.9877)
Orange point: horizontal coordinate 1.025, calculated vertical coordinate 0.98773. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.025, into the displayed formula to obtain 0.98773 on the vertical axis. Values are rounded for display.

Scope. Near-tip linear-elastic asymptotic field, outside the process zone; the singular tip itself is excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Cohesive-zone model · Example 1

Triangular cohesive traction law

Problem & parameters. Choose peak traction at half the complete-separation opening and linear loading/softening branches.

t/tmax⁡={2dd≤0.52(1−d)d>0.5t/t_{\max}=\begin{cases}2d&d\le0.5\\2(1-d)&d>0.5\end{cases}

Solution. Connect (0,0), (δc/2,tmax), and (δc,0). The work of separation is the triangle area tmaxδc/2.

Cohesive-zone model: Triangular cohesive traction law. Horizontal axis: Opening d = δ / δc (dimensionless). Vertical axis: Traction / peak traction (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Opening d = δ / δc (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Traction / peak traction (dimensionless) Triangular cohesive traction law Stated analytical example Worked point: (0.5, 1)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Monotonic prescribed cohesive law; unloading and mixed-mode effects are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Phase-field fracture model · Example 1

One-dimensional AT2 crack profile

Problem & parameters. Minimize the isolated AT2 crack-surface functional with d(0)=1 and d→0 far from the crack, without mechanical driving away from x=0.

d(x)=e−∣x∣/ℓd(x)=e^{-|x|/\ell}

Solution. The Euler equation is d−ℓ²d″=0 on each half-line. Select decaying exponentials and enforce symmetry.

Phase-field fracture model: One-dimensional AT2 crack profile. Horizontal axis: Distance from crack x / ℓ (dimensionless). Vertical axis: Damage d (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −4 −2 0 2 4 Distance from crack x / ℓ (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Damage d (dimensionless) One-dimensional AT2 crack profile Stated analytical example Worked point: (0, 1)
Orange point: horizontal coordinate 0, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Stationary isolated crack-profile benchmark, not a coupled fracture-growth solution.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Paris fatigue crack-growth law · Example 1

Paris-law crack growth for exponent two

Problem & parameters. Use da/dN=C(ΔK)² and ΔK=Δσ√(πa) with constant stress range and geometry factor one.

a/a0=eN/N∗,N∗=(CΔσ2π)−1a/a_0=e^{N/N_*},\quad N_*=(C\Delta\sigma^2\pi)^{-1}

Solution. Substitute ΔK to obtain da/dN = CΔσ²πa. Separate variables and apply a(0)=a₀.

Paris fatigue crack-growth law: Paris-law crack growth for exponent two. Horizontal axis: Cycle count N / N* (dimensionless). Vertical axis: Crack length a / a₀ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Cycle count N / N* (dimensionless) 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 Crack length a / a₀ (dimensionless) Paris-law crack growth for exponent two Stated analytical example Worked point: (0.75, 2.117)
Orange point: horizontal coordinate 0.75, calculated vertical coordinate 2.117. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.75, into the displayed formula to obtain 2.117 on the vertical axis. Values are rounded for display.

Scope. Only within the Paris regime; threshold, instability, and changing geometry are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Miner cumulative damage rule · Example 1

Single-amplitude fatigue damage

Problem & parameters. Apply constant-amplitude cycles with a fixed fatigue life Nf.

D=n/NfD=n/N_f

Solution. Miner’s sum has one term n/Nf; the conventional failure threshold is D=1.

Miner cumulative damage rule: Single-amplitude fatigue damage. Horizontal axis: Applied cycles / failure cycles n/Nf (dimensionless). Vertical axis: Accumulated damage D (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Applied cycles / failure cycles n/Nf (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Accumulated damage D (dimensionless) Single-amplitude fatigue damage Stated analytical example Worked point: (0.5, 0.5)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.5 on the vertical axis. Values are rounded for display.

Scope. Linear accumulation hypothesis, not a physical guarantee of failure at exactly D=1.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Archard wear model · Example 1

Wear volume versus sliding distance

Problem & parameters. Hold wear coefficient k, normal force W, and hardness H constant.

VH/(kWs∗)=s/s∗VH/(kWs_*)=s/s_*

Solution. Integrate dV/ds = kW/H from zero initial wear.

Archard wear model: Wear volume versus sliding distance. Horizontal axis: Sliding distance s / s* (dimensionless). Vertical axis: Scaled wear volume VH / kWs* (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Sliding distance s / s* (dimensionless) 0 1 2 3 4 5 Scaled wear volume VH / kWs* (dimensionless) Wear volume versus sliding distance Stated analytical example Worked point: (2.5, 2.5)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 2.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 2.5 on the vertical axis. Values are rounded for display.

Scope. Steady Archard wear regime with no changes in contact, debris, or material properties.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Newton–Euler rigid-body model · Example 1

Constant-force translation

Problem & parameters. A rigid body starts at rest with constant net force-to-mass ratio 1 m/s² along one axis and zero net torque.

x(t)=12at2,a=1  m/s2x(t)=\tfrac12at^2,\quad a=1\;\mathrm{m/s^2}

Solution. Newton’s law gives constant acceleration. Integrate twice with zero initial position and velocity.

Newton–Euler rigid-body model: Constant-force translation. Horizontal axis: Elapsed time t (s). Vertical axis: Displacement x (m). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Elapsed time t (s) 0 2 4 6 8 10 12 14 Displacement x (m) Constant-force translation Stated analytical example Worked point: (2.5, 3.125)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 3.125. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 3.125 on the vertical axis. Values are rounded for display.

Scope. Single translational degree of freedom; the remaining forces, torques, and rotational motion are set to zero.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Six-degree-of-freedom flight model · Example 1

Constant-force translation

Problem & parameters. A rigid body starts at rest with constant net force-to-mass ratio 1 m/s² along one axis and zero net torque.

x(t)=12at2,a=1  m/s2x(t)=\tfrac12at^2,\quad a=1\;\mathrm{m/s^2}

Solution. Newton’s law gives constant acceleration. Integrate twice with zero initial position and velocity.

Six-degree-of-freedom flight model: Constant-force translation. Horizontal axis: Elapsed time t (s). Vertical axis: Displacement x (m). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Elapsed time t (s) 0 2 4 6 8 10 12 14 Displacement x (m) Constant-force translation Stated analytical example Worked point: (2.5, 3.125)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 3.125. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 3.125 on the vertical axis. Values are rounded for display.

Scope. Single translational degree of freedom; the remaining forces, torques, and rotational motion are set to zero.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Lagrangian mechanics · Example 1

One conservative vibration mode

Problem & parameters. Choose a single unconstrained linear mode with zero damping, initial displacement A, and zero velocity.

q/A=cos⁡(ωt)q/A=\cos(\omega t)

Solution. Either force balance or the quadratic energy gives q″+ω²q=0. Apply the initial conditions to select the cosine.

Lagrangian mechanics: One conservative vibration mode. Horizontal axis: Phase ωt (radian). Vertical axis: Displacement / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 2 4 6 8 10 12 Phase ωt (radian) −1.0 −0.5 0.0 0.5 1.0 Displacement / initial amplitude (dimensionless) One conservative vibration mode Stated analytical example Worked point: (6.283, 1)
Orange point: horizontal coordinate 6.2832, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 6.2832, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Exact single harmonic mode; multibody constraints and other modal couplings are absent.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Hamiltonian mechanics · Example 1

One conservative vibration mode

Problem & parameters. Choose a single unconstrained linear mode with zero damping, initial displacement A, and zero velocity.

q/A=cos⁡(ωt)q/A=\cos(\omega t)

Solution. Either force balance or the quadratic energy gives q″+ω²q=0. Apply the initial conditions to select the cosine.

Hamiltonian mechanics: One conservative vibration mode. Horizontal axis: Phase ωt (radian). Vertical axis: Displacement / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 2 4 6 8 10 12 Phase ωt (radian) −1.0 −0.5 0.0 0.5 1.0 Displacement / initial amplitude (dimensionless) One conservative vibration mode Stated analytical example Worked point: (6.283, 1)
Orange point: horizontal coordinate 6.2832, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 6.2832, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Exact single harmonic mode; multibody constraints and other modal couplings are absent.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Mass–spring–damper model · Example 1

One conservative vibration mode

Problem & parameters. Choose a single unconstrained linear mode with zero damping, initial displacement A, and zero velocity.

q/A=cos⁡(ωt)q/A=\cos(\omega t)

Solution. Either force balance or the quadratic energy gives q″+ω²q=0. Apply the initial conditions to select the cosine.

Mass–spring–damper model: One conservative vibration mode. Horizontal axis: Phase ωt (radian). Vertical axis: Displacement / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 2 4 6 8 10 12 Phase ωt (radian) −1.0 −0.5 0.0 0.5 1.0 Displacement / initial amplitude (dimensionless) One conservative vibration mode Stated analytical example Worked point: (6.283, 1)
Orange point: horizontal coordinate 6.2832, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 6.2832, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Exact single harmonic mode; multibody constraints and other modal couplings are absent.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Multibody dynamics · Example 1

One conservative vibration mode

Problem & parameters. Choose a single unconstrained linear mode with zero damping, initial displacement A, and zero velocity.

q/A=cos⁡(ωt)q/A=\cos(\omega t)

Solution. Either force balance or the quadratic energy gives q″+ω²q=0. Apply the initial conditions to select the cosine.

Multibody dynamics: One conservative vibration mode. Horizontal axis: Phase ωt (radian). Vertical axis: Displacement / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 2 4 6 8 10 12 Phase ωt (radian) −1.0 −0.5 0.0 0.5 1.0 Displacement / initial amplitude (dimensionless) One conservative vibration mode Stated analytical example Worked point: (6.283, 1)
Orange point: horizontal coordinate 6.2832, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 6.2832, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Exact single harmonic mode; multibody constraints and other modal couplings are absent.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Duffing oscillator · Example 1

Duffing equilibrium force curve

Problem & parameters. For positive linear and cubic stiffness choose ℓ=√(k/β). Find the force needed to hold a static displacement.

F/(kℓ)=q+q3,q=x/ℓF/(k\ell)=q+q^3,\quad q=x/\ell

Solution. Set velocity and acceleration to zero in the Duffing equation. Normalize F=kx+βx³.

Duffing oscillator: Duffing equilibrium force curve. Horizontal axis: Static displacement x / ℓ (dimensionless). Vertical axis: Static force / kℓ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 Static displacement x / ℓ (dimensionless) −10 −5 0 5 10 Static force / kℓ (dimensionless) Duffing equilibrium force curve Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Static hardening equilibrium curve, not a forced nonlinear transient or resonance calculation.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Linear acoustic wave model · Example 1

Linear traveling-wave snapshot

Problem & parameters. Use a one-dimensional sinusoidal wave in a uniform, lossless linear medium. Plot the normalized field at time zero.

u(ξ,0)=sin⁡(2πξ)u(\xi,0)=\sin(2\pi\xi)

Solution. A sinusoidal traveling-wave solution is u = sin[2π(ξ−τ)]. Set τ = 0 to obtain the plotted snapshot.

Linear acoustic wave model: Linear traveling-wave snapshot. Horizontal axis: Position / wavelength (dimensionless). Vertical axis: Acoustic pressure / amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position / wavelength (dimensionless) −1.0 −0.5 0.0 0.5 1.0 Acoustic pressure / amplitude (dimensionless) Linear traveling-wave snapshot Stated analytical example Worked point: (0.5, 1.225e-16)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1.2246e-16. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1.2246e-16 on the vertical axis. Values are rounded for display.

Scope. An acoustic, electromagnetic, elastic, or linear Alfvén-wave reference as appropriate. For MHD this is the small transverse perturbation of a uniform magnetized equilibrium; for FDTD it is an exact target, not a discretized result.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Transmission-line acoustic model · Example 1

Linear traveling-wave snapshot

Problem & parameters. Use a one-dimensional sinusoidal wave in a uniform, lossless linear medium. Plot the normalized field at time zero.

u(ξ,0)=sin⁡(2πξ)u(\xi,0)=\sin(2\pi\xi)

Solution. A sinusoidal traveling-wave solution is u = sin[2π(ξ−τ)]. Set τ = 0 to obtain the plotted snapshot.

Transmission-line acoustic model: Linear traveling-wave snapshot. Horizontal axis: Position / wavelength (dimensionless). Vertical axis: Acoustic pressure / amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position / wavelength (dimensionless) −1.0 −0.5 0.0 0.5 1.0 Acoustic pressure / amplitude (dimensionless) Linear traveling-wave snapshot Stated analytical example Worked point: (0.5, 1.225e-16)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1.2246e-16. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1.2246e-16 on the vertical axis. Values are rounded for display.

Scope. An acoustic, electromagnetic, elastic, or linear Alfvén-wave reference as appropriate. For MHD this is the small transverse perturbation of a uniform magnetized equilibrium; for FDTD it is an exact target, not a discretized result.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Maxwell electromagnetic model · Example 1

Linear traveling-wave snapshot

Problem & parameters. Use a one-dimensional sinusoidal wave in a uniform, lossless linear medium. Plot the normalized field at time zero.

u(ξ,0)=sin⁡(2πξ)u(\xi,0)=\sin(2\pi\xi)

Solution. A sinusoidal traveling-wave solution is u = sin[2π(ξ−τ)]. Set τ = 0 to obtain the plotted snapshot.

Maxwell electromagnetic model: Linear traveling-wave snapshot. Horizontal axis: Position / wavelength (dimensionless). Vertical axis: Electric-field component / amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position / wavelength (dimensionless) −1.0 −0.5 0.0 0.5 1.0 Electric-field component / amplitude (dimensionless) Linear traveling-wave snapshot Stated analytical example Worked point: (0.5, 1.225e-16)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1.2246e-16. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1.2246e-16 on the vertical axis. Values are rounded for display.

Scope. An acoustic, electromagnetic, elastic, or linear Alfvén-wave reference as appropriate. For MHD this is the small transverse perturbation of a uniform magnetized equilibrium; for FDTD it is an exact target, not a discretized result.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Transmission-line electrical model · Example 1

Linear traveling-wave snapshot

Problem & parameters. Use a one-dimensional sinusoidal wave in a uniform, lossless linear medium. Plot the normalized field at time zero.

u(ξ,0)=sin⁡(2πξ)u(\xi,0)=\sin(2\pi\xi)

Solution. A sinusoidal traveling-wave solution is u = sin[2π(ξ−τ)]. Set τ = 0 to obtain the plotted snapshot.

Transmission-line electrical model: Linear traveling-wave snapshot. Horizontal axis: Position / wavelength (dimensionless). Vertical axis: Line voltage / amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position / wavelength (dimensionless) −1.0 −0.5 0.0 0.5 1.0 Line voltage / amplitude (dimensionless) Linear traveling-wave snapshot Stated analytical example Worked point: (0.5, 1.225e-16)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1.2246e-16. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1.2246e-16 on the vertical axis. Values are rounded for display.

Scope. An acoustic, electromagnetic, elastic, or linear Alfvén-wave reference as appropriate. For MHD this is the small transverse perturbation of a uniform magnetized equilibrium; for FDTD it is an exact target, not a discretized result.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Elastic seismic-wave model · Example 1

Linear traveling-wave snapshot

Problem & parameters. Use a one-dimensional sinusoidal wave in a uniform, lossless linear medium. Plot the normalized field at time zero.

u(ξ,0)=sin⁡(2πξ)u(\xi,0)=\sin(2\pi\xi)

Solution. A sinusoidal traveling-wave solution is u = sin[2π(ξ−τ)]. Set τ = 0 to obtain the plotted snapshot.

Elastic seismic-wave model: Linear traveling-wave snapshot. Horizontal axis: Position / wavelength (dimensionless). Vertical axis: Elastic displacement / amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position / wavelength (dimensionless) −1.0 −0.5 0.0 0.5 1.0 Elastic displacement / amplitude (dimensionless) Linear traveling-wave snapshot Stated analytical example Worked point: (0.5, 1.225e-16)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1.2246e-16. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1.2246e-16 on the vertical axis. Values are rounded for display.

Scope. An acoustic, electromagnetic, elastic, or linear Alfvén-wave reference as appropriate. For MHD this is the small transverse perturbation of a uniform magnetized equilibrium; for FDTD it is an exact target, not a discretized result.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Magnetohydrodynamics (MHD) · Example 1

Linear traveling-wave snapshot

Problem & parameters. Use a one-dimensional sinusoidal wave in a uniform, lossless linear medium. Plot the normalized field at time zero.

u(ξ,0)=sin⁡(2πξ)u(\xi,0)=\sin(2\pi\xi)

Solution. A sinusoidal traveling-wave solution is u = sin[2π(ξ−τ)]. Set τ = 0 to obtain the plotted snapshot.

Magnetohydrodynamics (MHD): Linear traveling-wave snapshot. Horizontal axis: Position / wavelength (dimensionless). Vertical axis: Transverse velocity perturbation / amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position / wavelength (dimensionless) −1.0 −0.5 0.0 0.5 1.0 Transverse velocity perturbation / amplitude (dimensionless) Linear traveling-wave snapshot Stated analytical example Worked point: (0.5, 1.225e-16)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1.2246e-16. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1.2246e-16 on the vertical axis. Values are rounded for display.

Scope. An acoustic, electromagnetic, elastic, or linear Alfvén-wave reference as appropriate. For MHD this is the small transverse perturbation of a uniform magnetized equilibrium; for FDTD it is an exact target, not a discretized result.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Finite-difference time-domain (FDTD) · Example 1

Linear traveling-wave snapshot

Problem & parameters. Use a one-dimensional sinusoidal wave in a uniform, lossless linear medium. Plot the normalized field at time zero.

u(ξ,0)=sin⁡(2πξ)u(\xi,0)=\sin(2\pi\xi)

Solution. A sinusoidal traveling-wave solution is u = sin[2π(ξ−τ)]. Set τ = 0 to obtain the plotted snapshot.

Finite-difference time-domain (FDTD): Linear traveling-wave snapshot. Horizontal axis: Position / wavelength (dimensionless). Vertical axis: Electric-field component / amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position / wavelength (dimensionless) −1.0 −0.5 0.0 0.5 1.0 Electric-field component / amplitude (dimensionless) Linear traveling-wave snapshot Stated analytical example Worked point: (0.5, 1.225e-16)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1.2246e-16. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1.2246e-16 on the vertical axis. Values are rounded for display.

Scope. An acoustic, electromagnetic, elastic, or linear Alfvén-wave reference as appropriate. For MHD this is the small transverse perturbation of a uniform magnetized equilibrium; for FDTD it is an exact target, not a discretized result.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Helmholtz acoustic model · Example 1

One-dimensional standing acoustic mode

Problem & parameters. Solve p″+k²p=0 with pressure-release endpoints and choose the first nonzero eigenmode.

p(x)/P=sin⁡(πx/L),k=π/Lp(x)/P=\sin(\pi x/L),\quad k=\pi/L

Solution. Both endpoint conditions select kL=π. Normalize the remaining arbitrary amplitude by its maximum.

Helmholtz acoustic model: One-dimensional standing acoustic mode. Horizontal axis: Position x / L (dimensionless). Vertical axis: Pressure amplitude / P (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Pressure amplitude / P (dimensionless) One-dimensional standing acoustic mode Stated analytical example Worked point: (0.5, 1)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Electrostatic Poisson model · Example 1

Uniform-charge potential between grounded planes

Problem & parameters. Solve φ″ = −ρ/ε for constant charge density between φ(0)=φ(L)=0.

ϕ/(ρL2/ϵ)=12ξ(1−ξ)\phi/(\rho L^2/\epsilon)=\tfrac12\xi(1-\xi)

Solution. Integrate the constant second derivative twice. The grounded endpoints fix both integration constants.

Electrostatic Poisson model: Uniform-charge potential between grounded planes. Horizontal axis: Position ξ = x / L (dimensionless). Vertical axis: Scaled electrostatic potential (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position ξ = x / L (dimensionless) 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 Scaled electrostatic potential (dimensionless) Uniform-charge potential between grounded planes Stated analytical example Worked point: (0.5, 0.125)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.125. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.125 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Magnetostatic model · Example 1

Magnetic field around a straight wire

Problem & parameters. Consider the exterior of a long straight wire of radius a carrying steady current I in vacuum.

B/(μ0I/2πa)=a/rB/(\mu_0 I/2\pi a)=a/r

Solution. Ampère’s law around a circle gives 2πrB=μ0I.

Magnetostatic model: Magnetic field around a straight wire. Horizontal axis: Radius from wire r / a (dimensionless). Vertical axis: Magnetic field / surface value (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Radius from wire r / a (dimensionless) 0.2 0.4 0.6 0.8 1.0 Magnetic field / surface value (dimensionless) Magnetic field around a straight wire Stated analytical example Worked point: (3, 0.3333)
Orange point: horizontal coordinate 3, calculated vertical coordinate 0.33333. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3, into the displayed formula to obtain 0.33333 on the vertical axis. Values are rounded for display.

Scope. Exterior field of an ideal long wire; end effects are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Eddy-current model · Example 1

AC skin-depth amplitude

Problem & parameters. A sinusoidal magnetic field penetrates a homogeneous conducting half-space with skin depth δ.

∣B(x)∣/∣B(0)∣=e−x/δ|B(x)|/|B(0)|=e^{-x/\delta}

Solution. The diffusion equation at angular frequency ω has a decaying complex solution exp[−(1+i)x/δ]. Its amplitude is exp(−x/δ).

Eddy-current model: AC skin-depth amplitude. Horizontal axis: Depth x / skin depth δ (dimensionless). Vertical axis: Magnetic-field amplitude fraction (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Depth x / skin depth δ (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Magnetic-field amplitude fraction (dimensionless) AC skin-depth amplitude Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Linear conductor with constant conductivity and permeability; displacement current neglected.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Magnetic-circuit model · Example 1

Linear magnetic circuit

Problem & parameters. Use a single magnetic circuit of fixed reluctance ℛ with no leakage.

Φ/Φ∗=(NI)/(RΦ∗)\Phi/\Phi_*=(NI)/(\mathcal R\Phi_*)

Solution. Solve NI=ℛΦ for flux.

Magnetic-circuit model: Linear magnetic circuit. Horizontal axis: Magnetomotive force / ℛΦ* (dimensionless). Vertical axis: Magnetic flux Φ / Φ* (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Magnetomotive force / ℛΦ* (dimensionless) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Magnetic flux Φ / Φ* (dimensionless) Linear magnetic circuit Stated analytical example Worked point: (1.5, 1.5)
Orange point: horizontal coordinate 1.5, calculated vertical coordinate 1.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5, into the displayed formula to obtain 1.5 on the vertical axis. Values are rounded for display.

Scope. Linear unsaturated material and fixed geometry.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Jiles–Atherton hysteresis model · Example 1

Anhysteretic magnetization curve

Problem & parameters. Evaluate the Langevin-form anhysteretic component of a Jiles–Atherton model, using its zero-field limit M=0.

Man/Ms=coth⁡h−1/hM_{an}/M_s=\coth h-1/h

Solution. Insert h=He/a into Ms[coth(h)−1/h]. The apparent singularity is removable; the small-field slope is 1/3.

Jiles–Atherton hysteresis model: Anhysteretic magnetization curve. Horizontal axis: Effective field / anhysteretic scale (dimensionless). Vertical axis: Anhysteretic magnetization / saturation (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −4 −2 0 2 4 Effective field / anhysteretic scale (dimensionless) −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 Anhysteretic magnetization / saturation (dimensionless) Anhysteretic magnetization curve Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Anhysteretic reference only, not the history-dependent hysteresis loop.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Geometrical optics · Example 1

Refraction from air into glass

Problem & parameters. A ray crosses a plane interface from index 1 into index 1.5.

θ2=arcsin⁡[sin⁡(θ1)/1.5]\theta_2=\arcsin[\sin(\theta_1)/1.5]

Solution. Use Snell’s law n1 sin θ1=n2 sin θ2 and solve for the refracted angle.

Geometrical optics: Refraction from air into glass. Horizontal axis: Incident angle θ₁ (degree). Vertical axis: Refracted angle θ₂ (degree). image/svg+xml IICSM analytical illustration / Matplotlib 0 10 20 30 40 50 60 70 80 Incident angle θ₁ (degree) 0 10 20 30 40 Refracted angle θ₂ (degree) Refraction from air into glass Stated analytical example Worked point: (40, 25.37)
Orange point: horizontal coordinate 40, calculated vertical coordinate 25.374. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 40, into the displayed formula to obtain 25.374 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Scalar diffraction model · Example 1

Single-slit far-field diffraction

Problem & parameters. Illuminate a slit of width a uniformly with monochromatic coherent light and observe the Fraunhofer pattern.

I/I0=[sin⁡uu]2I/I_0=\left[\frac{\sin u}{u}\right]^2

Solution. Integrate the phase factor across the slit to obtain sinc amplitude; square its magnitude. Use the continuous limit I/I0=1 at u=0.

Scalar diffraction model: Single-slit far-field diffraction. Horizontal axis: Diffraction coordinate u = πa sinθ / λ (dimensionless). Vertical axis: Intensity I / I₀ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −8 −6 −4 −2 0 2 4 6 8 Diffraction coordinate u = πa sinθ / λ (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Intensity I / I₀ (dimensionless) Single-slit far-field diffraction Stated analytical example Worked point: (0, 1)
Orange point: horizontal coordinate 0, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Gaussian beam model · Example 1

Gaussian beam transverse intensity

Problem & parameters. At a fixed axial plane, take a fundamental paraxial Gaussian beam with 1/e² intensity radius w.

I(r)/I(0)=e−2(r/w)2I(r)/I(0)=e^{-2(r/w)^2}

Solution. Square the Gaussian field amplitude exp(−r²/w²) to obtain its intensity.

Gaussian beam model: Gaussian beam transverse intensity. Horizontal axis: Transverse position / beam radius w (dimensionless). Vertical axis: Relative intensity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 Transverse position / beam radius w (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Relative intensity (dimensionless) Gaussian beam transverse intensity Stated analytical example Worked point: (0, 1)
Orange point: horizontal coordinate 0, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. One transverse cut at a fixed plane; w changes with axial distance.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Drude–Lorentz optical model · Example 1

Lossless Drude dielectric response

Problem & parameters. Take the free-electron Drude limit with zero collision rate, no Lorentz resonances, and background permittivity one.

ϵr=1−(ωp/ω)2\epsilon_r=1-(\omega_p/\omega)^2

Solution. Solve the harmonic free-electron displacement equation and insert the induced polarization into D=ε0E+P.

Drude–Lorentz optical model: Lossless Drude dielectric response. Horizontal axis: Frequency ω / plasma frequency ωp (dimensionless). Vertical axis: Relative permittivity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.5 1.0 1.5 2.0 2.5 3.0 Frequency ω / plasma frequency ωp (dimensionless) −3 −2 −1 0 1 Relative permittivity (dimensionless) Lossless Drude dielectric response Stated analytical example Worked point: (1.75, 0.6735)
Orange point: horizontal coordinate 1.75, calculated vertical coordinate 0.67347. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.75, into the displayed formula to obtain 0.67347 on the vertical axis. Values are rounded for display.

Scope. Lossless frequency-domain special case; the zero-frequency singular point is excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Lumped RLC circuit model · Example 1

RC charging limit of an RLC circuit

Problem & parameters. Set inductance to zero and apply a voltage step Vs to a series resistor and initially uncharged capacitor.

VC/Vs=1−e−t/(RC)V_C/V_s=1-e^{-t/(RC)}

Solution. Kirchhoff’s law gives RCV′+V=Vs. Solve the first-order initial-value problem.

Lumped RLC circuit model: RC charging limit of an RLC circuit. Horizontal axis: Time t / RC (dimensionless). Vertical axis: Capacitor voltage / supply (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time t / RC (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Capacitor voltage / supply (dimensionless) RC charging limit of an RLC circuit Stated analytical example Worked point: (2.5, 0.9179)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.91792. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.91792 on the vertical axis. Values are rounded for display.

Scope. RC limiting circuit, not a general second-order RLC transient.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Shockley diode model · Example 1

Ideal diode current

Problem & parameters. Evaluate the Shockley diode law without series resistance or reverse breakdown.

I/Is=eV/(nVT)−1I/I_s=e^{V/(nV_T)}-1

Solution. Substitute the thermal-voltage-scaled bias into the exponential current law.

Shockley diode model: Ideal diode current. Horizontal axis: Voltage V / nVT (dimensionless). Vertical axis: Current I / saturation current (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −3 −2 −1 0 1 2 3 Voltage V / nVT (dimensionless) 0 5 10 15 20 Current I / saturation current (dimensionless) Ideal diode current Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Ebers–Moll transistor model · Example 1

Forward-active transistor collector current

Problem & parameters. Use the forward-active Ebers–Moll branch and neglect the reverse junction contribution.

IC/(αFIES)=eVBE/VT−1I_C/(\alpha_F I_{ES})=e^{V_{BE}/V_T}-1

Solution. Keep the αFIES[exp(VBE/VT)−1] term and divide by its prefactor.

Ebers–Moll transistor model: Forward-active transistor collector current. Horizontal axis: Base–emitter voltage / thermal voltage (dimensionless). Vertical axis: Scaled collector current (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Base–emitter voltage / thermal voltage (dimensionless) 0 10 20 30 40 50 60 Scaled collector current (dimensionless) Forward-active transistor collector current Stated analytical example Worked point: (2, 6.389)
Orange point: horizontal coordinate 2, calculated vertical coordinate 6.3891. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2, into the displayed formula to obtain 6.3891 on the vertical axis. Values are rounded for display.

Scope. Forward-active approximation; no saturation, Early effect, or breakdown.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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MOSFET square-law model · Example 1

Long-channel MOSFET saturation

Problem & parameters. Use a long-channel MOSFET in strong-inversion saturation with constant mobility and no channel-length modulation.

ID/(βV∗2/2)=[(VGS−Vth)/V∗]2I_D/(\beta V_*^2/2)=[(V_{GS}-V_{th})/V_*]^2

Solution. Set VDS at or above overdrive and integrate the gradual-channel charge relation to obtain ID=β(VGS−Vth)²/2.

MOSFET square-law model: Long-channel MOSFET saturation. Horizontal axis: Gate overdrive / reference voltage (dimensionless). Vertical axis: Scaled drain current (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Gate overdrive / reference voltage (dimensionless) 0 2 4 6 8 10 Scaled drain current (dimensionless) Long-channel MOSFET saturation Stated analytical example Worked point: (1.5, 2.25)
Orange point: horizontal coordinate 1.5, calculated vertical coordinate 2.25. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5, into the displayed formula to obtain 2.25 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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BSIM compact-model family · Example 1

Weak-inversion current benchmark

Problem & parameters. Use an ideal weak-inversion exponential trend at fixed drain bias as a compact-model check.

ID/I∗=e(VGS−V∗)/(nVT)I_D/I_*=e^{(V_{GS}-V_*)/(nV_T)}

Solution. A Boltzmann subthreshold charge law gives current proportional to exp(VGS/nVT); normalize at VGS=V*.

BSIM compact-model family: Weak-inversion current benchmark. Horizontal axis: Scaled gate bias (VGS−V*) / nVT (dimensionless). Vertical axis: Drain current / reference current (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −4 −3 −2 −1 0 1 Scaled gate bias (VGS−V*) / nVT (dimensionless) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Drain current / reference current (dimensionless) Weak-inversion current benchmark Stated analytical example Worked point: (-1.5, 0.2231)
Orange point: horizontal coordinate -1.5, calculated vertical coordinate 0.22313. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, -1.5, into the displayed formula to obtain 0.22313 on the vertical axis. Values are rounded for display.

Scope. Asymptotic benchmark only; not the complete BSIM equations or a result from a foundry model card.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Drift–diffusion semiconductor model · Example 1

Uniform-carrier drift current

Problem & parameters. Take uniform electron density n, fixed mobility μ, and a low-field steady state. The density gradient is zero.

J/(qnμE∗)=E/E∗J/(qn\mu E_*)=E/E_*

Solution. The diffusion contribution vanishes. Evaluate the conventional drift-current magnitude law J=qnμE.

Drift–diffusion semiconductor model: Uniform-carrier drift current. Horizontal axis: Electric field E / E* (dimensionless). Vertical axis: Current density / qnμE* (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 Electric field E / E* (dimensionless) −2 −1 0 1 2 Current density / qnμE* (dimensionless) Uniform-carrier drift current Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Low-field isothermal drift limit; carrier heating and higher hydrodynamic moments are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Hydrodynamic carrier model · Example 1

Uniform-carrier drift current

Problem & parameters. Take uniform electron density n, fixed mobility μ, and a low-field steady state. The density gradient is zero.

J/(qnμE∗)=E/E∗J/(qn\mu E_*)=E/E_*

Solution. The diffusion contribution vanishes. Evaluate the conventional drift-current magnitude law J=qnμE.

Hydrodynamic carrier model: Uniform-carrier drift current. Horizontal axis: Electric field E / E* (dimensionless). Vertical axis: Current density / qnμE* (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 Electric field E / E* (dimensionless) −2 −1 0 1 2 Current density / qnμE* (dimensionless) Uniform-carrier drift current Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Low-field isothermal drift limit; carrier heating and higher hydrodynamic moments are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Nernst equilibrium potential · Example 1

Equilibrium potential versus activity ratio

Problem & parameters. For Ox+ne− ⇌ Red use ideal specified activities and fixed temperature.

nF(E−E∘)/(RT)=ln⁡(aox/ared)nF(E-E^\circ)/(RT)=\ln(a_{ox}/a_{red})

Solution. Set the reaction electrochemical free-energy change to zero and rearrange the Nernst relation.

Nernst equilibrium potential: Equilibrium potential versus activity ratio. Horizontal axis: Oxidized / reduced activity ratio (dimensionless). Vertical axis: Scaled equilibrium potential (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 2 4 6 8 10 Oxidized / reduced activity ratio (dimensionless) −2 −1 0 1 2 Scaled equilibrium potential (dimensionless) Equilibrium potential versus activity ratio Stated analytical example Worked point: (5.05, 1.619)
Orange point: horizontal coordinate 5.05, calculated vertical coordinate 1.6194. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 5.05, into the displayed formula to obtain 1.6194 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Butler–Volmer kinetics · Example 1

Symmetric Butler–Volmer polarization

Problem & parameters. Set anodic and cathodic transfer coefficients to one half, with one-electron charge convention.

j/j0=2sinh⁡(η∗/2),η∗=Fη/(RT)j/j_0=2\sinh(\eta_*/2),\quad\eta_*=F\eta/(RT)

Solution. Subtract the two exponentials in Butler–Volmer to obtain twice the hyperbolic sine.

Butler–Volmer kinetics: Symmetric Butler–Volmer polarization. Horizontal axis: Overpotential Fη / RT (dimensionless). Vertical axis: Current density / exchange current (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −4 −3 −2 −1 0 1 2 3 4 Overpotential Fη / RT (dimensionless) −8 −6 −4 −2 0 2 4 6 8 Current density / exchange current (dimensionless) Symmetric Butler–Volmer polarization Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Tafel approximation · Example 1

Anodic Tafel relation

Problem & parameters. Use the anodic high-overpotential regime where the cathodic exponential is negligible.

αFη/(RT)=ln⁡(j/j0)\alpha F\eta/(RT)=\ln(j/j_0)

Solution. From j≈j0 exp(αFη/RT), take logarithms and solve for overpotential.

Tafel approximation: Anodic Tafel relation. Horizontal axis: Anodic current / exchange current (dimensionless). Vertical axis: Scaled overpotential αFη / RT (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 20 40 60 80 100 Anodic current / exchange current (dimensionless) 2.5 3.0 3.5 4.0 4.5 Scaled overpotential αFη / RT (dimensionless) Anodic Tafel relation Stated analytical example Worked point: (55, 4.007)
Orange point: horizontal coordinate 55, calculated vertical coordinate 4.0073. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 55, into the displayed formula to obtain 4.0073 on the vertical axis. Values are rounded for display.

Scope. Asymptotic approximation, plotted well above j/j0=1; not valid near equilibrium.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Poisson–Nernst–Planck model · Example 1

Screened potential in a dilute electrolyte

Problem & parameters. At zero ionic flux, linearize a symmetric dilute electrolyte near equilibrium next to a planar wall.

ϕ/ϕ0=e−x/λD\phi/\phi_0=e^{-x/\lambda_D}

Solution. Boltzmann ionic populations linearize Poisson’s equation to φ″=φ/λD². Select the decaying solution and impose the wall potential.

Poisson–Nernst–Planck model: Screened potential in a dilute electrolyte. Horizontal axis: Distance / Debye length (dimensionless). Vertical axis: Potential / wall potential (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Distance / Debye length (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Potential / wall potential (dimensionless) Screened potential in a dilute electrolyte Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Debye–Hückel equilibrium limit of PNP, requiring |zFφ|≪RT; no driven ionic transport.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Doyle–Fuller–Newman (DFN/P2D) model · Example 1

Spherical-particle average concentration balance

Problem & parameters. Start with a spherical active particle of radius R and mean concentration c*. Impose constant outward molar flux jout.

cˉ/c∗=1−3τ,τ=joutt/(Rc∗)\bar c/c_* =1-3\tau,\quad\tau=j_{out}t/(Rc_*)

Solution. Integrate spherical diffusion over particle volume: d(c̄)/dt=−(surface/volume)jout=−3jout/R. Apply the initial average.

Doyle–Fuller–Newman (DFN/P2D) model: Spherical-particle average concentration balance. Horizontal axis: Extraction coordinate jout t / Rc* (dimensionless). Vertical axis: Particle-average concentration / c* (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.00 0.05 0.10 0.15 0.20 0.25 Extraction coordinate jout t / Rc* (dimensionless) 0.2 0.4 0.6 0.8 1.0 Particle-average concentration / c* (dimensionless) Spherical-particle average concentration balance Stated analytical example Worked point: (0.125, 0.625)
Orange point: horizontal coordinate 0.125, calculated vertical coordinate 0.625. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.125, into the displayed formula to obtain 0.625 on the vertical axis. Values are rounded for display.

Scope. Exact particle mass balance shared by DFN, SPM, and SPMe. It does not give the radial profile, terminal voltage, electrolyte dynamics, or a usable-capacity prediction.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Single-particle battery model (SPM) · Example 1

Spherical-particle average concentration balance

Problem & parameters. Start with a spherical active particle of radius R and mean concentration c*. Impose constant outward molar flux jout.

cˉ/c∗=1−3τ,τ=joutt/(Rc∗)\bar c/c_* =1-3\tau,\quad\tau=j_{out}t/(Rc_*)

Solution. Integrate spherical diffusion over particle volume: d(c̄)/dt=−(surface/volume)jout=−3jout/R. Apply the initial average.

Single-particle battery model (SPM): Spherical-particle average concentration balance. Horizontal axis: Extraction coordinate jout t / Rc* (dimensionless). Vertical axis: Particle-average concentration / c* (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.00 0.05 0.10 0.15 0.20 0.25 Extraction coordinate jout t / Rc* (dimensionless) 0.2 0.4 0.6 0.8 1.0 Particle-average concentration / c* (dimensionless) Spherical-particle average concentration balance Stated analytical example Worked point: (0.125, 0.625)
Orange point: horizontal coordinate 0.125, calculated vertical coordinate 0.625. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.125, into the displayed formula to obtain 0.625 on the vertical axis. Values are rounded for display.

Scope. Exact particle mass balance shared by DFN, SPM, and SPMe. It does not give the radial profile, terminal voltage, electrolyte dynamics, or a usable-capacity prediction.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Single-particle model with electrolyte (SPMe) · Example 1

Spherical-particle average concentration balance

Problem & parameters. Start with a spherical active particle of radius R and mean concentration c*. Impose constant outward molar flux jout.

cˉ/c∗=1−3τ,τ=joutt/(Rc∗)\bar c/c_* =1-3\tau,\quad\tau=j_{out}t/(Rc_*)

Solution. Integrate spherical diffusion over particle volume: d(c̄)/dt=−(surface/volume)jout=−3jout/R. Apply the initial average.

Single-particle model with electrolyte (SPMe): Spherical-particle average concentration balance. Horizontal axis: Extraction coordinate jout t / Rc* (dimensionless). Vertical axis: Particle-average concentration / c* (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.00 0.05 0.10 0.15 0.20 0.25 Extraction coordinate jout t / Rc* (dimensionless) 0.2 0.4 0.6 0.8 1.0 Particle-average concentration / c* (dimensionless) Spherical-particle average concentration balance Stated analytical example Worked point: (0.125, 0.625)
Orange point: horizontal coordinate 0.125, calculated vertical coordinate 0.625. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.125, into the displayed formula to obtain 0.625 on the vertical axis. Values are rounded for display.

Scope. Exact particle mass balance shared by DFN, SPM, and SPMe. It does not give the radial profile, terminal voltage, electrolyte dynamics, or a usable-capacity prediction.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Equivalent-circuit battery model · Example 1

Battery polarization under a current step

Problem & parameters. Apply a constant current I to an initially relaxed single-RC battery polarization branch.

Vp/(IRp)=1−e−t/(RpCp)V_p/(IR_p)=1-e^{-t/(R_pC_p)}

Solution. Solve CpVp′+Vp/Rp=I. The terminal-voltage drop also includes any separate series ohmic resistance.

Equivalent-circuit battery model: Battery polarization under a current step. Horizontal axis: Time / polarization RC constant (dimensionless). Vertical axis: Polarization voltage / IRp (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / polarization RC constant (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Polarization voltage / IRp (dimensionless) Battery polarization under a current step Stated analytical example Worked point: (2.5, 0.9179)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.91792. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.91792 on the vertical axis. Values are rounded for display.

Scope. One branch with fixed parameters; state of charge and open-circuit voltage are held fixed.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Darcy porous-flow model · Example 1

Darcy flux versus pressure gradient

Problem & parameters. Let G=−dp/dx be positive, and hold permeability k and viscosity μ constant.

u/(kG∗/μ)=G/G∗u/(kG_*/\mu)=G/G_*

Solution. Solve μu/k = G.

Darcy porous-flow model: Darcy flux versus pressure gradient. Horizontal axis: Driving pressure gradient G / G* (dimensionless). Vertical axis: Scaled Darcy velocity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Driving pressure gradient G / G* (dimensionless) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Scaled Darcy velocity (dimensionless) Darcy flux versus pressure gradient Stated analytical example Worked point: (1.5, 1.5)
Orange point: horizontal coordinate 1.5, calculated vertical coordinate 1.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5, into the displayed formula to obtain 1.5 on the vertical axis. Values are rounded for display.

Scope. Single-phase creeping flow in a homogeneous porous medium.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Brinkman porous-flow model · Example 1

Brinkman flow between porous walls

Problem & parameters. Solve μe u″−μu/k+G=0 between no-slip walls ±H. Choose screening length ℓ=√(μe k/μ) and H/ℓ=2.

u/(kG/μ)=1−cosh⁡(x/ℓ)/cosh⁡(H/ℓ),H/ℓ=2u/(kG/\mu)=1-\cosh(x/\ell)/\cosh(H/\ell),\quad H/\ell=2

Solution. Add a constant particular solution kG/μ to the symmetric cosh homogeneous solution, then enforce the wall values.

Brinkman porous-flow model: Brinkman flow between porous walls. Horizontal axis: Transverse position x / H (dimensionless). Vertical axis: Velocity / Darcy bulk velocity (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Transverse position x / H (dimensionless) 0.0 0.2 0.4 0.6 0.8 Velocity / Darcy bulk velocity (dimensionless) Brinkman flow between porous walls Stated analytical example Worked point: (0, 0.7342)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0.7342. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0.7342 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Forchheimer model · Example 1

Forchheimer inertial pressure loss

Problem & parameters. Choose velocity and gradient scales so that the linear and quadratic drag coefficients are both one.

G/G∗=v+v2G/G_*=v+v^2

Solution. Substitute positive velocity into G=av+bv|v| and apply the chosen scaling.

Forchheimer model: Forchheimer inertial pressure loss. Horizontal axis: Scaled positive velocity v (dimensionless). Vertical axis: Scaled pressure gradient (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Scaled positive velocity v (dimensionless) 0 2 4 6 8 10 12 Scaled pressure gradient (dimensionless) Forchheimer inertial pressure loss Stated analytical example Worked point: (1.5, 3.75)
Orange point: horizontal coordinate 1.5, calculated vertical coordinate 3.75. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5, into the displayed formula to obtain 3.75 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Richards equation · Example 1

Linearized unsaturated-head relaxation

Problem & parameters. Linearize moisture capacity and hydraulic conductivity about a uniform reference state, neglect gravity, and solve the resulting diffusion equation on a slab.

u(ξ,τ)=sin⁡(πξ)e−π2τ,τ=0.1u(\xi,\tau)=\sin(\pi\xi)e^{-\pi^2\tau},\quad\tau=0.1

Solution. The sine satisfies both zero end values. Its second derivative is −π² times itself; the amplitude solves a′ = −π²a.

Richards equation: Linearized unsaturated-head relaxation. Horizontal axis: Position x / L (dimensionless). Vertical axis: Pressure-head perturbation / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.1 0.2 0.3 0.4 Pressure-head perturbation / initial amplitude (dimensionless) Linearized unsaturated-head relaxation Stated analytical example Worked point: (0.5, 0.3727)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.37271. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.37271 on the vertical axis. Values are rounded for display.

Scope. Constant-coefficient linearization of Richards’ equation. The nonlinear retention and conductivity changes are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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van Genuchten retention model · Example 1

van Genuchten water retention

Problem & parameters. Choose n=2 and m=1−1/n=1/2 for a drying retention curve.

Se=[1+(α∣h∣)2]−1/2S_e=[1+(\alpha|h|)^2]^{-1/2}

Solution. Insert the chosen parameters into Se=[1+(α|h|)^n]^−m.

van Genuchten retention model: van Genuchten water retention. Horizontal axis: Scaled suction α|h| (dimensionless). Vertical axis: Effective saturation Se (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Scaled suction α|h| (dimensionless) 0.2 0.4 0.6 0.8 1.0 Effective saturation Se (dimensionless) van Genuchten water retention Stated analytical example Worked point: (2.5, 0.3714)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.37139. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.37139 on the vertical axis. Values are rounded for display.

Scope. Retention relation only; hysteresis and conductivity are not evaluated.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Biot poroelasticity · Example 1

A single consolidation pressure mode

Problem & parameters. Use one-dimensional linear consolidation with drained ends and an initial excess pore-pressure mode sin(πx/L). Plot cvt/L²=0.1.

u(ξ,τ)=sin⁡(πξ)e−π2τ,τ=0.1u(\xi,\tau)=\sin(\pi\xi)e^{-\pi^2\tau},\quad\tau=0.1

Solution. The sine satisfies both zero end values. Its second derivative is −π² times itself; the amplitude solves a′ = −π²a.

Biot poroelasticity: A single consolidation pressure mode. Horizontal axis: Position x / L (dimensionless). Vertical axis: Excess pore pressure / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.1 0.2 0.3 0.4 Excess pore pressure / initial amplitude (dimensionless) A single consolidation pressure mode Stated analytical example Worked point: (0.5, 0.3727)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.37271. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.37271 on the vertical axis. Values are rounded for display.

Scope. Exact single-mode Terzaghi solution and a compatible one-dimensional poroelastic reduction; not an arbitrary initial loading history.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Terzaghi consolidation model · Example 1

A single consolidation pressure mode

Problem & parameters. Use one-dimensional linear consolidation with drained ends and an initial excess pore-pressure mode sin(πx/L). Plot cvt/L²=0.1.

u(ξ,τ)=sin⁡(πξ)e−π2τ,τ=0.1u(\xi,\tau)=\sin(\pi\xi)e^{-\pi^2\tau},\quad\tau=0.1

Solution. The sine satisfies both zero end values. Its second derivative is −π² times itself; the amplitude solves a′ = −π²a.

Terzaghi consolidation model: A single consolidation pressure mode. Horizontal axis: Position x / L (dimensionless). Vertical axis: Excess pore pressure / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.1 0.2 0.3 0.4 Excess pore pressure / initial amplitude (dimensionless) A single consolidation pressure mode Stated analytical example Worked point: (0.5, 0.3727)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.37271. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.37271 on the vertical axis. Values are rounded for display.

Scope. Exact single-mode Terzaghi solution and a compatible one-dimensional poroelastic reduction; not an arbitrary initial loading history.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Modified Cam-Clay model · Example 1

Modified Cam-Clay yield ellipse

Problem & parameters. Hold preconsolidation pressure pc and critical-state slope M fixed. Plot the compression-positive yield locus.

q/(Mpc)=(p/pc)(1−p/pc)q/(Mp_c)=\sqrt{(p/p_c)(1-p/p_c)}

Solution. Solve q²+M²p(p−pc)=0 for the nonnegative q branch.

Modified Cam-Clay model: Modified Cam-Clay yield ellipse. Horizontal axis: Mean effective pressure p / pc (dimensionless). Vertical axis: Deviatoric stress q / Mpc (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Mean effective pressure p / pc (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 Deviatoric stress q / Mpc (dimensionless) Modified Cam-Clay yield ellipse Stated analytical example Worked point: (0.5, 0.5)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.5 on the vertical axis. Values are rounded for display.

Scope. Yield-surface geometry only; hardening and stress-path evolution are not solved.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Saint-Venant shallow-water model · Example 1

Linear shallow-water surface wave

Problem & parameters. Linearize shallow-water dynamics about rest at constant depth H; the wave speed is √(gH).

u(ξ,0)=sin⁡(2πξ)u(\xi,0)=\sin(2\pi\xi)

Solution. A sinusoidal traveling-wave solution is u = sin[2π(ξ−τ)]. Set τ = 0 to obtain the plotted snapshot.

Saint-Venant shallow-water model: Linear shallow-water surface wave. Horizontal axis: Position / wavelength (dimensionless). Vertical axis: Surface elevation / wave amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position / wavelength (dimensionless) −1.0 −0.5 0.0 0.5 1.0 Surface elevation / wave amplitude (dimensionless) Linear shallow-water surface wave Stated analytical example Worked point: (0.5, 1.225e-16)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1.2246e-16. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1.2246e-16 on the vertical axis. Values are rounded for display.

Scope. Small free-surface perturbation in a constant-depth channel, without friction or dispersion.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Kinematic-wave routing · Example 1

Constant-speed routing pulse

Problem & parameters. Use the linear routing equation ht+hx=0 with initial Gaussian pulse exp(−x²).

h(x,1)=e−(x−1)2h(x,1)=e^{-(x-1)^2}

Solution. The pulse is constant along characteristics x−t, hence it translates without changing shape.

Kinematic-wave routing: Constant-speed routing pulse. Horizontal axis: Channel position x (dimensionless). Vertical axis: Flow-depth perturbation h (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −3 −2 −1 0 1 2 3 4 5 Channel position x (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Flow-depth perturbation h (dimensionless) Constant-speed routing pulse Stated analytical example Worked point: (1, 1)
Orange point: horizontal coordinate 1, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Constant-celerity reduction; nonlinear depth-dependent routing can distort or steepen the pulse.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Rainfall–runoff model · Example 1

Linear-reservoir recession

Problem & parameters. After rainfall stops, let storage S obey S′=−S/K and outflow Q=S/K. Normalize either by its initial value.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Rainfall–runoff model: Linear-reservoir recession. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Reservoir outflow / initial outflow (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Reservoir outflow / initial outflow (dimensionless) Linear-reservoir recession Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. One-reservoir rainfall–runoff component; no new rain, infiltration, or additional routing stores.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Advection–dispersion groundwater model · Example 1

Dispersing tracer plume

Problem & parameters. On an infinite line take velocity one, dispersion coefficient 0.1, and initial concentration exp(−x²).

c(x,1)=11.4e−(x−1)2/1.4c(x,1)=\frac1{\sqrt{1.4}}e^{-(x-1)^2/1.4}

Solution. Translate the Gaussian by vt and broaden its squared width to 1+4Dt, adjusting amplitude to conserve mass.

Advection–dispersion groundwater model: Dispersing tracer plume. Horizontal axis: Distance x (dimensionless). Vertical axis: Tracer concentration (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −3 −2 −1 0 1 2 3 4 5 Distance x (dimensionless) 0.0 0.2 0.4 0.6 0.8 Tracer concentration (dimensionless) Dispersing tracer plume Stated analytical example Worked point: (1, 0.8452)
Orange point: horizontal coordinate 1, calculated vertical coordinate 0.84515. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1, into the displayed formula to obtain 0.84515 on the vertical axis. Values are rounded for display.

Scope. Homogeneous advection–dispersion with no reactions or sorption.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Numerical weather prediction · Example 1

Isothermal hydrostatic atmosphere

Problem & parameters. Use an ideal gas at constant temperature and constant gravity, with density ρ0 at height zero.

ρ(z)/ρ0=e−z/H\rho(z)/\rho_0=e^{-z/H}

Solution. Combine dp/dz=−ρg with p=ρRsT; integrate dρ/dz=−ρ/H, H=RsT/g.

Numerical weather prediction: Isothermal hydrostatic atmosphere. Horizontal axis: Height / pressure scale height H (dimensionless). Vertical axis: Density / base density (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Height / pressure scale height H (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Density / base density (dimensionless) Isothermal hydrostatic atmosphere Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Hydrostatic column benchmark only. For stellar structure this approximates a thin isothermal layer, not an entire star; radiation, convection, and dynamics are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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General circulation model (GCM) · Example 1

Isothermal hydrostatic atmosphere

Problem & parameters. Use an ideal gas at constant temperature and constant gravity, with density ρ0 at height zero.

ρ(z)/ρ0=e−z/H\rho(z)/\rho_0=e^{-z/H}

Solution. Combine dp/dz=−ρg with p=ρRsT; integrate dρ/dz=−ρ/H, H=RsT/g.

General circulation model (GCM): Isothermal hydrostatic atmosphere. Horizontal axis: Height / pressure scale height H (dimensionless). Vertical axis: Density / base density (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Height / pressure scale height H (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Density / base density (dimensionless) Isothermal hydrostatic atmosphere Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Hydrostatic column benchmark only. For stellar structure this approximates a thin isothermal layer, not an entire star; radiation, convection, and dynamics are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Stellar structure model · Example 1

Isothermal hydrostatic atmosphere

Problem & parameters. Use an ideal gas at constant temperature and constant gravity, with density ρ0 at height zero.

ρ(z)/ρ0=e−z/H\rho(z)/\rho_0=e^{-z/H}

Solution. Combine dp/dz=−ρg with p=ρRsT; integrate dρ/dz=−ρ/H, H=RsT/g.

Stellar structure model: Isothermal hydrostatic atmosphere. Horizontal axis: Height / pressure scale height H (dimensionless). Vertical axis: Density / base density (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Height / pressure scale height H (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Density / base density (dimensionless) Isothermal hydrostatic atmosphere Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Hydrostatic column benchmark only. For stellar structure this approximates a thin isothermal layer, not an entire star; radiation, convection, and dynamics are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Earth system model (ESM) · Example 1

One-box climate response to a forcing step

Problem & parameters. For a constant radiative-forcing step F, use CΔT′=F−λΔT with positive linear feedback parameter λ and initially zero anomaly.

ΔT/(F/λ)=1−e−λt/C\Delta T/(F/\lambda)=1-e^{-\lambda t/C}

Solution. Apply an integrating factor to the one-box energy balance; the equilibrium anomaly is F/λ.

Earth system model (ESM): One-box climate response to a forcing step. Horizontal axis: Time λt / heat capacity C (dimensionless). Vertical axis: Temperature change / equilibrium change (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time λt / heat capacity C (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Temperature change / equilibrium change (dimensionless) One-box climate response to a forcing step Stated analytical example Worked point: (2.5, 0.9179)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.91792. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.91792 on the vertical axis. Values are rounded for display.

Scope. Reduced global-mean energy balance. For ESM this is an illustrative diagnostic reduction, not a full Earth-system forecast.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Energy-balance climate model · Example 1

One-box climate response to a forcing step

Problem & parameters. For a constant radiative-forcing step F, use CΔT′=F−λΔT with positive linear feedback parameter λ and initially zero anomaly.

ΔT/(F/λ)=1−e−λt/C\Delta T/(F/\lambda)=1-e^{-\lambda t/C}

Solution. Apply an integrating factor to the one-box energy balance; the equilibrium anomaly is F/λ.

Energy-balance climate model: One-box climate response to a forcing step. Horizontal axis: Time λt / heat capacity C (dimensionless). Vertical axis: Temperature change / equilibrium change (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time λt / heat capacity C (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Temperature change / equilibrium change (dimensionless) One-box climate response to a forcing step Stated analytical example Worked point: (2.5, 0.9179)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.91792. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.91792 on the vertical axis. Values are rounded for display.

Scope. Reduced global-mean energy balance. For ESM this is an illustrative diagnostic reduction, not a full Earth-system forecast.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Ocean circulation model · Example 1

Linear barotropic ocean-wave reference

Problem & parameters. Use a constant-depth, nonrotating, inviscid shallow-water reduction of ocean circulation.

u(ξ,0)=sin⁡(2πξ)u(\xi,0)=\sin(2\pi\xi)

Solution. A sinusoidal traveling-wave solution is u = sin[2π(ξ−τ)]. Set τ = 0 to obtain the plotted snapshot.

Ocean circulation model: Linear barotropic ocean-wave reference. Horizontal axis: Position / wavelength (dimensionless). Vertical axis: Surface elevation / wave amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position / wavelength (dimensionless) −1.0 −0.5 0.0 0.5 1.0 Surface elevation / wave amplitude (dimensionless) Linear barotropic ocean-wave reference Stated analytical example Worked point: (0.5, 1.225e-16)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1.2246e-16. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1.2246e-16 on the vertical axis. Values are rounded for display.

Scope. Single linear barotropic mode; rotation, stratification, mixing, and realistic boundaries are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Sea-ice thermodynamic-dynamic model · Example 1

Conduction-limited ice growth

Problem & parameters. Assume zero initial thickness, fixed surface-to-freezing temperature difference ΔT, and conductive flux kΔT/h through the ice.

h/ℓ=t/t∗h/\ell=\sqrt{t/t_*}

Solution. Balance latent heat: ρLh′=kΔT/h. Integrate h²=2kΔTt/(ρL) and choose t*=ρLℓ²/(2kΔT).

Sea-ice thermodynamic-dynamic model: Conduction-limited ice growth. Horizontal axis: Elapsed time / growth time t* (dimensionless). Vertical axis: Ice thickness h / ℓ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Elapsed time / growth time t* (dimensionless) 0.0 0.5 1.0 1.5 2.0 Ice thickness h / ℓ (dimensionless) Conduction-limited ice growth Stated analytical example Worked point: (2, 1.414)
Orange point: horizontal coordinate 2, calculated vertical coordinate 1.4142. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2, into the displayed formula to obtain 1.4142 on the vertical axis. Values are rounded for display.

Scope. Stefan growth limit with no ocean heat flux, snow insulation, or ice dynamics.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Lifting-line model · Example 1

Finite-wing lift slope

Problem & parameters. Use lifting-line theory for an ideal elliptically loaded wing of aspect ratio eight and two-dimensional slope 2π per radian.

CL=2πα1+2/(e AR),e=1, AR=8C_L=\frac{2\pi\alpha}{1+2/(e\,AR)},\quad e=1,\ AR=8

Solution. The induced angle reduces the effective angle. Solve CL=a0[α−CL/(πeAR)] for CL.

Lifting-line model: Finite-wing lift slope. Horizontal axis: Angle of attack α (degree). Vertical axis: Lift coefficient CL (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −6 −4 −2 0 2 4 6 Angle of attack α (degree) −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 Lift coefficient CL (dimensionless) Finite-wing lift slope Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Small-angle attached-flow approximation; no stall prediction.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Blade-element momentum model · Example 1

Ideal actuator-disk power

Problem & parameters. Use the ideal nonrotating actuator-disk limit underlying axial momentum theory.

CP=4a(1−a)2C_P=4a(1-a)^2

Solution. Mass, momentum, and energy balances give the displayed coefficient. Differentiating yields a maximum 16/27 at a=1/3.

Blade-element momentum model: Ideal actuator-disk power. Horizontal axis: Axial induction factor a (dimensionless). Vertical axis: Power coefficient CP (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.1 0.2 0.3 0.4 0.5 Axial induction factor a (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Power coefficient CP (dimensionless) Ideal actuator-disk power Stated analytical example Worked point: (0.25, 0.5625)
Orange point: horizontal coordinate 0.25, calculated vertical coordinate 0.5625. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.25, into the displayed formula to obtain 0.5625 on the vertical axis. Values are rounded for display.

Scope. Momentum-theory benchmark for BEM; blade geometry, swirl, drag, tip losses, and high-induction corrections are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Bicycle vehicle model · Example 1

Steady kinematic bicycle turning

Problem & parameters. Assume low-speed rolling without tire slip for a vehicle of wheelbase L.

κL=tan⁡δ\kappa L=\tan\delta

Solution. The front-wheel geometry gives turn radius R=L/tanδ; curvature is 1/R.

Bicycle vehicle model: Steady kinematic bicycle turning. Horizontal axis: Steering angle δ (degree). Vertical axis: Path curvature × wheelbase κL (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −30 −20 −10 0 10 20 30 Steering angle δ (degree) −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 Path curvature × wheelbase κL (dimensionless) Steady kinematic bicycle turning Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Kinematic limit, not a high-speed dynamic tire-force model.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Quarter-car suspension model · Example 1

Wheel-hop-free suspension mode

Problem & parameters. Hold the unsprung mass fixed, set damping to zero, and release the sprung mass from displacement A.

z/A=cos⁡(ωt)z/A=\cos(\omega t)

Solution. The reduced quarter-car equation is ms z″+ks z=0; ω=√(ks/ms).

Quarter-car suspension model: Wheel-hop-free suspension mode. Horizontal axis: Phase ωt (radian). Vertical axis: Sprung displacement / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 2 4 6 8 10 12 Phase ωt (radian) −1.0 −0.5 0.0 0.5 1.0 Sprung displacement / initial amplitude (dimensionless) Wheel-hop-free suspension mode Stated analytical example Worked point: (6.283, 1)
Orange point: horizontal coordinate 6.2832, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 6.2832, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Single-mode constrained reduction, not the full two-degree-of-freedom road response.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Pacejka tire model · Example 1

Illustrative Magic Formula tire force

Problem & parameters. Choose B=10, C=1.3, E=0, zero offsets, and fixed load in the basic Pacejka Magic Formula.

F/D=sin⁡[1.3arctan⁡(10s)]F/D=\sin[1.3\arctan(10s)]

Solution. With E=0 the curvature correction drops out. Evaluate the sine of the scaled arctangent.

Pacejka tire model: Illustrative Magic Formula tire force. Horizontal axis: Slip ratio s (dimensionless). Vertical axis: Force / peak-scale D (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 Slip ratio s (dimensionless) −1.0 −0.5 0.0 0.5 1.0 Force / peak-scale D (dimensionless) Illustrative Magic Formula tire force Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Illustrative coefficients, not a calibrated tire or a combined-slip model.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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AC power-flow model · Example 1

Lossless power-angle relation

Problem & parameters. Use two fixed voltage magnitudes connected by a purely reactive line; for a generator use the analogous fixed internal-voltage coupling.

P/(V1V2/X)=sin⁡δP/(V_1V_2/X)=\sin\delta

Solution. The lossless AC circuit gives P=(V1V2/X)sinδ.

AC power-flow model: Lossless power-angle relation. Horizontal axis: Electrical angle difference δ (radian). Vertical axis: Transferred power / coupling scale (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 Electrical angle difference δ (radian) −1.0 −0.5 0.0 0.5 1.0 Transferred power / coupling scale (dimensionless) Lossless power-angle relation Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Steady electrical-power term; the swing-equation rotor transient and voltage dynamics are not solved.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Swing-equation generator model · Example 1

Lossless power-angle relation

Problem & parameters. Use two fixed voltage magnitudes connected by a purely reactive line; for a generator use the analogous fixed internal-voltage coupling.

P/(V1V2/X)=sin⁡δP/(V_1V_2/X)=\sin\delta

Solution. The lossless AC circuit gives P=(V1V2/X)sinδ.

Swing-equation generator model: Lossless power-angle relation. Horizontal axis: Electrical angle difference δ (radian). Vertical axis: Transferred power / coupling scale (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 Electrical angle difference δ (radian) −1.0 −0.5 0.0 0.5 1.0 Transferred power / coupling scale (dimensionless) Lossless power-angle relation Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. Steady electrical-power term; the swing-equation rotor transient and voltage dynamics are not solved.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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DC power-flow approximation · Example 1

Small-angle DC power flow

Problem & parameters. Use nearly equal fixed bus voltage magnitudes, negligible resistance, and small angle difference.

P/(V1V2/X)≈δP/(V_1V_2/X)\approx\delta

Solution. Linearize sinδ≈δ in the lossless AC transfer formula.

DC power-flow approximation: Small-angle DC power flow. Horizontal axis: Small angle difference δ (radian). Vertical axis: Scaled active power (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −0.20 −0.15 −0.10 −0.05 0.00 0.05 0.10 0.15 0.20 Small angle difference δ (radian) −0.2 −0.1 0.0 0.1 0.2 Scaled active power (dimensionless) Small-angle DC power flow Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. DC power-flow approximation; it does not calculate reactive power or voltage magnitudes.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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State-space model · Example 1

First-order unit-step response

Problem & parameters. Use the scalar state equation y′+y=1 with y(0)=0, or transfer function 1/(s+1).

y(τ)=1−e−τy(\tau)=1-e^{-\tau}

Solution. The homogeneous response is Ce^−τ and the constant particular response is one. The initial state gives C=−1.

State-space model: First-order unit-step response. Horizontal axis: Time / system time constant (dimensionless). Vertical axis: Output / final value (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / system time constant (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Output / final value (dimensionless) First-order unit-step response Stated analytical example Worked point: (2.5, 0.9179)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.91792. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.91792 on the vertical axis. Values are rounded for display.

Scope. Exact linear plant reference. For bond graphs/electrical analogs use a single storage-and-resistance element; for HIL this is a reference trajectory, not measured hardware data.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Transfer-function model · Example 1

First-order unit-step response

Problem & parameters. Use the scalar state equation y′+y=1 with y(0)=0, or transfer function 1/(s+1).

y(τ)=1−e−τy(\tau)=1-e^{-\tau}

Solution. The homogeneous response is Ce^−τ and the constant particular response is one. The initial state gives C=−1.

Transfer-function model: First-order unit-step response. Horizontal axis: Time / system time constant (dimensionless). Vertical axis: Output / final value (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / system time constant (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Output / final value (dimensionless) First-order unit-step response Stated analytical example Worked point: (2.5, 0.9179)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.91792. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.91792 on the vertical axis. Values are rounded for display.

Scope. Exact linear plant reference. For bond graphs/electrical analogs use a single storage-and-resistance element; for HIL this is a reference trajectory, not measured hardware data.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Bond-graph model · Example 1

First-order unit-step response

Problem & parameters. Use the scalar state equation y′+y=1 with y(0)=0, or transfer function 1/(s+1).

y(τ)=1−e−τy(\tau)=1-e^{-\tau}

Solution. The homogeneous response is Ce^−τ and the constant particular response is one. The initial state gives C=−1.

Bond-graph model: First-order unit-step response. Horizontal axis: Time / system time constant (dimensionless). Vertical axis: Output / final value (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / system time constant (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Output / final value (dimensionless) First-order unit-step response Stated analytical example Worked point: (2.5, 0.9179)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.91792. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.91792 on the vertical axis. Values are rounded for display.

Scope. Exact linear plant reference. For bond graphs/electrical analogs use a single storage-and-resistance element; for HIL this is a reference trajectory, not measured hardware data.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Electrical analog model · Example 1

First-order unit-step response

Problem & parameters. Use the scalar state equation y′+y=1 with y(0)=0, or transfer function 1/(s+1).

y(τ)=1−e−τy(\tau)=1-e^{-\tau}

Solution. The homogeneous response is Ce^−τ and the constant particular response is one. The initial state gives C=−1.

Electrical analog model: First-order unit-step response. Horizontal axis: Time / system time constant (dimensionless). Vertical axis: Output / final value (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / system time constant (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Output / final value (dimensionless) First-order unit-step response Stated analytical example Worked point: (2.5, 0.9179)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.91792. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.91792 on the vertical axis. Values are rounded for display.

Scope. Exact linear plant reference. For bond graphs/electrical analogs use a single storage-and-resistance element; for HIL this is a reference trajectory, not measured hardware data.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Hardware-in-the-loop model · Example 1

First-order unit-step response

Problem & parameters. Use the scalar state equation y′+y=1 with y(0)=0, or transfer function 1/(s+1).

y(τ)=1−e−τy(\tau)=1-e^{-\tau}

Solution. The homogeneous response is Ce^−τ and the constant particular response is one. The initial state gives C=−1.

Hardware-in-the-loop model: First-order unit-step response. Horizontal axis: Time / system time constant (dimensionless). Vertical axis: Output / final value (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / system time constant (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Output / final value (dimensionless) First-order unit-step response Stated analytical example Worked point: (2.5, 0.9179)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.91792. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.91792 on the vertical axis. Values are rounded for display.

Scope. Exact linear plant reference. For bond graphs/electrical analogs use a single storage-and-resistance element; for HIL this is a reference trajectory, not measured hardware data.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Hybrid dynamical model · Example 1

Bouncing-ball flight and one impact

Problem & parameters. Drop a ball from 1 m with g=9.81 m/s². At first ground contact reverse velocity and multiply its magnitude by restitution e=0.8. Plot before the second impact.

h(t)={1−12gt2t≤tiegti(t−ti)−12g(t−ti)2t>tih(t)=\begin{cases}1-\tfrac12gt^2&t\le t_i\\ egt_i(t-t_i)-\tfrac12g(t-t_i)^2&t>t_i\end{cases}

Solution. The first impact occurs at ti=√(2/g). Integrate constant gravity before and after the velocity reset with continuous height.

Hybrid dynamical model: Bouncing-ball flight and one impact. Horizontal axis: Time t (s). Vertical axis: Ball height h (m). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Time t (s) 0.0 0.2 0.4 0.6 0.8 1.0 Ball height h (m) Bouncing-ball flight and one impact Stated analytical example Worked point: (0.55, 0.3014)
Orange point: horizontal coordinate 0.55, calculated vertical coordinate 0.30139. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.55, into the displayed formula to obtain 0.30139 on the vertical axis. Values are rounded for display.

Scope. Ideal instantaneous first bounce; air resistance and contact deformation are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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System-dynamics stock-flow model · Example 1

Stock with constant inflow and linear outflow

Problem & parameters. An initially empty stock receives constant inflow q and drains at rate kS.

S/(q/k)=1−e−ktS/(q/k)=1-e^{-kt}

Solution. Solve S′=q−kS with S(0)=0.

System-dynamics stock-flow model: Stock with constant inflow and linear outflow. Horizontal axis: Time kt (dimensionless). Vertical axis: Stock / equilibrium stock (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time kt (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Stock / equilibrium stock (dimensionless) Stock with constant inflow and linear outflow Stated analytical example Worked point: (2.5, 0.9179)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.91792. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.91792 on the vertical axis. Values are rounded for display.

Scope. Single stock, constant coefficients, and no delays or saturation.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Discrete-event simulation · Example 1

Deterministic event accumulation

Problem & parameters. Identical events occur at Δt,2Δt,… with zero events completed at t=0.

N(t)=⌊t/Δt⌋N(t)=\lfloor t/\Delta t\rfloor

Solution. Count the positive integer multiples of Δt not exceeding t. The floor function gives the exact event count.

Discrete-event simulation: Deterministic event accumulation. Horizontal axis: Time / event interval Δt (dimensionless). Vertical axis: Cumulative completed events (count). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Time / event interval Δt (dimensionless) 0 1 2 3 4 5 6 Cumulative completed events (count) Deterministic event accumulation Stated analytical example Worked point: (3, 3)
Orange point: horizontal coordinate 3, calculated vertical coordinate 3. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3, into the displayed formula to obtain 3 on the vertical axis. Values are rounded for display.

Scope. Simple scheduled-event benchmark; a discrete-event model need not have periodic arrivals.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Agent-based physical-system model · Example 1

Mean position of independent moving agents

Problem & parameters. Agents start at mean position zero, have constant mean velocity 1 m/s, and do not interact.

⟨x(t)⟩=x0+vt\langle x(t)\rangle=x_0+vt

Solution. Each agent has x=x0+vt. Average this relation over agents; the mean is linear in time.

Agent-based physical-system model: Mean position of independent moving agents. Horizontal axis: Time t (s). Vertical axis: Ensemble mean position (m). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time t (s) 0 1 2 3 4 5 Ensemble mean position (m) Mean position of independent moving agents Stated analytical example Worked point: (2.5, 2.5)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 2.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 2.5 on the vertical axis. Values are rounded for display.

Scope. Noninteracting kinematic benchmark; not an emergent many-agent simulation.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Markov state model · Example 1

Two-state continuous-time occupation

Problem & parameters. Two states exchange population at equal rate k. Initially all probability is in state one.

P1(t)=12(1+e−2kt)P_1(t)=\tfrac12(1+e^{-2kt})

Solution. Use P2=1−P1 in P1′=−kP1+kP2. Solve the resulting first-order equation.

Markov state model: Two-state continuous-time occupation. Horizontal axis: Time kt (dimensionless). Vertical axis: Probability of state 1 (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Time kt (dimensionless) 0.5 0.6 0.7 0.8 0.9 1.0 Probability of state 1 (dimensionless) Two-state continuous-time occupation Stated analytical example Worked point: (2, 0.5092)
Orange point: horizontal coordinate 2, calculated vertical coordinate 0.50916. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2, into the displayed formula to obtain 0.50916 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Kalman state estimator · Example 1

Kalman gain versus measurement noise

Problem & parameters. For one scalar measurement with observation coefficient one, hold the positive prior variance fixed.

K=P−P−+R=11+R/P−K=\frac{P^-}{P^-+R}=\frac1{1+R/P^-}

Solution. Insert H=1 into K=P−H/(H²P−+R).

Kalman state estimator: Kalman gain versus measurement noise. Horizontal axis: Measurement / prior variance R/P⁻ (dimensionless). Vertical axis: Scalar Kalman gain (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 2 4 6 8 10 Measurement / prior variance R/P⁻ (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Scalar Kalman gain (dimensionless) Kalman gain versus measurement noise Stated analytical example Worked point: (5, 0.1667)
Orange point: horizontal coordinate 5, calculated vertical coordinate 0.16667. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 5, into the displayed formula to obtain 0.16667 on the vertical axis. Values are rounded for display.

Scope. Single measurement update; not a full dynamic filter trajectory.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Model predictive control · Example 1

One-step unconstrained predictive control

Problem & parameters. Let xnext=x+u and minimize (x+u)²+ρu² with ρ=1 and no constraints.

u∗=−x1+ρ,ρ=1u_*=-\frac{x}{1+\rho},\quad\rho=1

Solution. Differentiate the quadratic cost with respect to u, set 2(x+u)+2ρu=0, and solve.

Model predictive control: One-step unconstrained predictive control. Horizontal axis: Initial state x (dimensionless). Vertical axis: Optimal input u* (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 Initial state x (dimensionless) −1.0 −0.5 0.0 0.5 1.0 Optimal input u* (dimensionless) One-step unconstrained predictive control Stated analytical example Worked point: (0, -0)
Orange point: horizontal coordinate 0, calculated vertical coordinate -0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain -0 on the vertical axis. Values are rounded for display.

Scope. Analytical horizon-one MPC example; longer horizons and constraints change the feedback law.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Hodgkin–Huxley membrane model · Example 1

Passive membrane voltage relaxation

Problem & parameters. Set sodium and potassium conductances to zero, hold leak reversal potential EL fixed, and normalize V−EL by its initial value. Use τ=gLt/Cm.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Hodgkin–Huxley membrane model: Passive membrane voltage relaxation. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Membrane voltage excess / initial excess (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Membrane voltage excess / initial excess (dimensionless) Passive membrane voltage relaxation Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Passive leak-only reduction of Hodgkin–Huxley; action potentials and voltage-dependent gates are deliberately excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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FitzHugh–Nagumo model · Example 1

FitzHugh–Nagumo voltage nullcline

Problem & parameters. For v′=v−v³/3−w+I set I=0 and find the zero-fast-derivative curve.

w=v−v3/3,I=0w=v-v^3/3,\quad I=0

Solution. Set v′=0 and solve for w.

FitzHugh–Nagumo model: FitzHugh–Nagumo voltage nullcline. Horizontal axis: Fast variable v (dimensionless). Vertical axis: Recovery variable w (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −2 −1 0 1 2 Fast variable v (dimensionless) −3 −2 −1 0 1 2 3 Recovery variable w (dimensionless) FitzHugh–Nagumo voltage nullcline Stated analytical example Worked point: (0, 0)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0 on the vertical axis. Values are rounded for display.

Scope. A phase-plane nullcline, not a trajectory or the complete system equilibrium; equilibria also lie on the recovery nullcline.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Hill muscle model · Example 1

Hill force–velocity curve

Problem & parameters. Use (F+a)(v+b)=(F0+a)b, with a/F0=0.25 and vmax=bF0/a.

F/F0=0.25(1−v/vmax⁡)0.25+v/vmax⁡F/F_0=\frac{0.25(1-v/v_{\max})}{0.25+v/v_{\max}}

Solution. Solve the hyperbolic force–velocity equation for F and substitute the normalized speed.

Hill muscle model: Hill force–velocity curve. Horizontal axis: Shortening speed / unloaded speed (dimensionless). Vertical axis: Muscle force / isometric force (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Shortening speed / unloaded speed (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Muscle force / isometric force (dimensionless) Hill force–velocity curve Stated analytical example Worked point: (0.5, 0.1667)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.16667. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.16667 on the vertical axis. Values are rounded for display.

Scope. Steady concentric shortening only; activation and length effects are held fixed.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Windkessel circulation model · Example 1

Windkessel diastolic pressure decay

Problem & parameters. With zero inflow, a two-element Windkessel discharges through resistance R from compliance C. Use τ=t/(RC) and normalize pressure above venous pressure.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Windkessel circulation model: Windkessel diastolic pressure decay. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Pressure above venous level / initial excess (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Pressure above venous level / initial excess (dimensionless) Windkessel diastolic pressure decay Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Constant-compliance diastolic interval, not a full pulsatile cardiac cycle.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Pennes bioheat model · Example 1

Uniform tissue heating with perfusion

Problem & parameters. Take spatially uniform tissue with constant heat source Q, blood heat-exchange coefficient W>0, and initial tissue temperature equal to arterial temperature Ta.

(T−Ta)/(Q/W)=1−e−Wt/(ρc)(T-T_a)/(Q/W)=1-e^{-Wt/(\rho c)}

Solution. The Pennes balance reduces to ρc T′=Q−W(T−Ta). Solve the linear initial-value problem.

Pennes bioheat model: Uniform tissue heating with perfusion. Horizontal axis: Perfusion relaxation time Wt / ρc (dimensionless). Vertical axis: Temperature rise / Q/W (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Perfusion relaxation time Wt / ρc (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Temperature rise / Q/W (dimensionless) Uniform tissue heating with perfusion Stated analytical example Worked point: (2.5, 0.9179)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.91792. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.91792 on the vertical axis. Values are rounded for display.

Scope. Uniform-temperature reduction; no spatial conduction, temperature-dependent perfusion, or safety prediction.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Reaction–diffusion morphogenesis model · Example 1

One linear reaction–diffusion mode

Problem & parameters. Solve ut=uxx−u with zero ends and initial sin(πx), then plot t=1.

u(x,1)=e−(π2+1)sin⁡(πx)u(x,1)=e^{-(\pi^2+1)}\sin(\pi x)

Solution. The Laplacian and decay each multiply the mode by a negative constant; its amplitude solves A′=−(π²+1)A.

Reaction–diffusion morphogenesis model: One linear reaction–diffusion mode. Horizontal axis: Position x / L (dimensionless). Vertical axis: Activator perturbation (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.5 1.0 1.5 2.0 Activator perturbation (dimensionless) 1e−5 One linear reaction–diffusion mode Stated analytical example Worked point: (0.5, 1.903e-05)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 1.9028e-05. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 1.9028e-05 on the vertical axis. Values are rounded for display.

Scope. One-species linear stable subproblem, not a two-species Turing pattern or nonlinear morphogenesis prediction.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Monod growth model · Example 1

Monod nutrient limitation

Problem & parameters. Evaluate growth rate at prescribed substrate concentration with fixed Monod parameters.

μ/μmax⁡=S/(Ks+S)\mu/\mu_{\max}=S/(K_s+S)

Solution. Normalize μ=μmax S/(Ks+S) by μmax and substitute S/Ks.

Monod growth model: Monod nutrient limitation. Horizontal axis: Substrate concentration S / Ks (dimensionless). Vertical axis: Growth rate / maximum rate (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 7 8 Substrate concentration S / Ks (dimensionless) 0.0 0.2 0.4 0.6 0.8 Growth rate / maximum rate (dimensionless) Monod nutrient limitation Stated analytical example Worked point: (4, 0.8)
Orange point: horizontal coordinate 4, calculated vertical coordinate 0.8. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 4, into the displayed formula to obtain 0.8 on the vertical axis. Values are rounded for display.

Scope. Growth-rate relation only; substrate depletion and biomass evolution are not integrated.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Physiologically based compartment model · Example 1

Single well-mixed compartment washout

Problem & parameters. After an initial dose, use one well-mixed compartment with first-order elimination, no further input, and τ=kt.

y(τ)=e−τy(\tau)=e^{-\tau}

Solution. Separate dy/dτ = −y, integrate ln(y) = −τ + C, and apply y(0) = 1.

Physiologically based compartment model: Single well-mixed compartment washout. Horizontal axis: Time / relaxation time (dimensionless). Vertical axis: Compartment concentration / initial concentration (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time / relaxation time (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Compartment concentration / initial concentration (dimensionless) Single well-mixed compartment washout Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. One-compartment limiting case; interorgan exchange, binding, and nonlinear metabolism are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Boltzmann kinetic equation · Example 1

Homogeneous Maxwellian velocity marginal

Problem & parameters. Take a spatially uniform equilibrium with zero drift and the normalized Gaussian velocity marginal. For collisionless plasma use zero fields and a neutralizing background.

vthf(v)=π−1/2e−(v/vth)2v_{th}f(v)=\pi^{-1/2}e^{-(v/v_{th})^2}

Solution. The homogeneous force-free streaming terms vanish. Maxwellian collisions balance for Boltzmann equilibrium; integrating the Gaussian fixes its normalization.

Boltzmann kinetic equation: Homogeneous Maxwellian velocity marginal. Horizontal axis: Velocity v / thermal speed (dimensionless). Vertical axis: Marginal probability density × thermal speed (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −3 −2 −1 0 1 2 3 Velocity v / thermal speed (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Marginal probability density × thermal speed (dimensionless) Homogeneous Maxwellian velocity marginal Stated analytical example Worked point: (0, 0.5642)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0.56419. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0.56419 on the vertical axis. Values are rounded for display.

Scope. Equilibrium distribution or exact kinetic benchmark. DSMC and PIC would estimate it using particles; this plot is not a finite-particle sample.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Vlasov–Poisson model · Example 1

Homogeneous Maxwellian velocity marginal

Problem & parameters. Take a spatially uniform equilibrium with zero drift and the normalized Gaussian velocity marginal. For collisionless plasma use zero fields and a neutralizing background.

vthf(v)=π−1/2e−(v/vth)2v_{th}f(v)=\pi^{-1/2}e^{-(v/v_{th})^2}

Solution. The homogeneous force-free streaming terms vanish. Maxwellian collisions balance for Boltzmann equilibrium; integrating the Gaussian fixes its normalization.

Vlasov–Poisson model: Homogeneous Maxwellian velocity marginal. Horizontal axis: Velocity v / thermal speed (dimensionless). Vertical axis: Marginal probability density × thermal speed (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −3 −2 −1 0 1 2 3 Velocity v / thermal speed (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Marginal probability density × thermal speed (dimensionless) Homogeneous Maxwellian velocity marginal Stated analytical example Worked point: (0, 0.5642)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0.56419. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0.56419 on the vertical axis. Values are rounded for display.

Scope. Equilibrium distribution or exact kinetic benchmark. DSMC and PIC would estimate it using particles; this plot is not a finite-particle sample.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Vlasov–Maxwell model · Example 1

Homogeneous Maxwellian velocity marginal

Problem & parameters. Take a spatially uniform equilibrium with zero drift and the normalized Gaussian velocity marginal. For collisionless plasma use zero fields and a neutralizing background.

vthf(v)=π−1/2e−(v/vth)2v_{th}f(v)=\pi^{-1/2}e^{-(v/v_{th})^2}

Solution. The homogeneous force-free streaming terms vanish. Maxwellian collisions balance for Boltzmann equilibrium; integrating the Gaussian fixes its normalization.

Vlasov–Maxwell model: Homogeneous Maxwellian velocity marginal. Horizontal axis: Velocity v / thermal speed (dimensionless). Vertical axis: Marginal probability density × thermal speed (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −3 −2 −1 0 1 2 3 Velocity v / thermal speed (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Marginal probability density × thermal speed (dimensionless) Homogeneous Maxwellian velocity marginal Stated analytical example Worked point: (0, 0.5642)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0.56419. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0.56419 on the vertical axis. Values are rounded for display.

Scope. Equilibrium distribution or exact kinetic benchmark. DSMC and PIC would estimate it using particles; this plot is not a finite-particle sample.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Direct simulation Monte Carlo (DSMC) · Example 1

Homogeneous Maxwellian velocity marginal

Problem & parameters. Take a spatially uniform equilibrium with zero drift and the normalized Gaussian velocity marginal. For collisionless plasma use zero fields and a neutralizing background.

vthf(v)=π−1/2e−(v/vth)2v_{th}f(v)=\pi^{-1/2}e^{-(v/v_{th})^2}

Solution. The homogeneous force-free streaming terms vanish. Maxwellian collisions balance for Boltzmann equilibrium; integrating the Gaussian fixes its normalization.

Direct simulation Monte Carlo (DSMC): Homogeneous Maxwellian velocity marginal. Horizontal axis: Velocity v / thermal speed (dimensionless). Vertical axis: Marginal probability density × thermal speed (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −3 −2 −1 0 1 2 3 Velocity v / thermal speed (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Marginal probability density × thermal speed (dimensionless) Homogeneous Maxwellian velocity marginal Stated analytical example Worked point: (0, 0.5642)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0.56419. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0.56419 on the vertical axis. Values are rounded for display.

Scope. Equilibrium distribution or exact kinetic benchmark. DSMC and PIC would estimate it using particles; this plot is not a finite-particle sample.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Particle-in-cell (PIC) · Example 1

Homogeneous Maxwellian velocity marginal

Problem & parameters. Take a spatially uniform equilibrium with zero drift and the normalized Gaussian velocity marginal. For collisionless plasma use zero fields and a neutralizing background.

vthf(v)=π−1/2e−(v/vth)2v_{th}f(v)=\pi^{-1/2}e^{-(v/v_{th})^2}

Solution. The homogeneous force-free streaming terms vanish. Maxwellian collisions balance for Boltzmann equilibrium; integrating the Gaussian fixes its normalization.

Particle-in-cell (PIC): Homogeneous Maxwellian velocity marginal. Horizontal axis: Velocity v / thermal speed (dimensionless). Vertical axis: Marginal probability density × thermal speed (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −3 −2 −1 0 1 2 3 Velocity v / thermal speed (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Marginal probability density × thermal speed (dimensionless) Homogeneous Maxwellian velocity marginal Stated analytical example Worked point: (0, 0.5642)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0.56419. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0.56419 on the vertical axis. Values are rounded for display.

Scope. Equilibrium distribution or exact kinetic benchmark. DSMC and PIC would estimate it using particles; this plot is not a finite-particle sample.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Neutron diffusion approximation · Example 1

Subcritical neutron-density diffusion mode

Problem & parameters. On a slab solve nτ=nξξ with zero extrapolated-end values and initial sin(πξ), ignoring reactions in this illustrative diffusion subproblem.

u(ξ,τ)=sin⁡(πξ)e−π2τ,τ=0.1u(\xi,\tau)=\sin(\pi\xi)e^{-\pi^2\tau},\quad\tau=0.1

Solution. The sine satisfies both zero end values. Its second derivative is −π² times itself; the amplitude solves a′ = −π²a.

Neutron diffusion approximation: Subcritical neutron-density diffusion mode. Horizontal axis: Position x / L (dimensionless). Vertical axis: Neutron-density perturbation / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.1 0.2 0.3 0.4 Neutron-density perturbation / initial amplitude (dimensionless) Subcritical neutron-density diffusion mode Stated analytical example Worked point: (0.5, 0.3727)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.37271. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.37271 on the vertical axis. Values are rounded for display.

Scope. Diffusion-only benchmark; absorption and fission terms would modify the mode growth/decay rate.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Point reactor kinetics · Example 1

Prompt-only subcritical neutron decay

Problem & parameters. Set delayed-neutron fraction and external source to zero, take constant negative reactivity ρ, and prompt generation time Λ.

n/n0=e−τ,τ=∣ρ∣t/Λn/n_0=e^{-\tau},\quad\tau=|\rho|t/\Lambda

Solution. Point kinetics reduces to n′=(ρ/Λ)n. Integrate with n(0)=n0.

Point reactor kinetics: Prompt-only subcritical neutron decay. Horizontal axis: Subcritical prompt time |ρ|t / Λ (dimensionless). Vertical axis: Neutron population / initial population (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Subcritical prompt time |ρ|t / Λ (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Neutron population / initial population (dimensionless) Prompt-only subcritical neutron decay Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Prompt-only idealization, not a realistic startup, shutdown, or reactor-safety calculation.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Bateman decay-chain model · Example 1

Daughter buildup in a two-step decay chain

Problem & parameters. Initially N1=N10 and N2=0. Let the parent decay to the daughter with λ2=2λ1 and unit branching fraction.

N2/N10=e−λ1t−e−2λ1tN_2/N_{10}=e^{-\lambda_1t}-e^{-2\lambda_1t}

Solution. Solve N1=N10exp(−λ1t). Insert into N2′+λ2N2=λ1N1 and integrate using an integrating factor.

Bateman decay-chain model: Daughter buildup in a two-step decay chain. Horizontal axis: Time λ₁t (dimensionless). Vertical axis: Daughter population / initial parent (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Time λ₁t (dimensionless) 0.00 0.05 0.10 0.15 0.20 0.25 Daughter population / initial parent (dimensionless) Daughter buildup in a two-step decay chain Stated analytical example Worked point: (2.5, 0.07535)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.075347. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.075347 on the vertical axis. Values are rounded for display.

Scope. Analytical solution or constitutive evaluation for the stated special case. It is not a general solution of the full model family.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Newtonian gravitational N-body model · Example 1

Circular two-body orbital coordinate

Problem & parameters. Reduce an isolated gravitational system to two point masses with total mass M in a circular relative orbit of radius a.

x/a=cos⁡(nt),n=GM/a3x/a=\cos(nt),\quad n=\sqrt{GM/a^3}

Solution. Balance relative centripetal acceleration n²a against GM/a². The Cartesian x coordinate then follows a cosine.

Newtonian gravitational N-body model: Circular two-body orbital coordinate. Horizontal axis: Orbital phase nt (radian). Vertical axis: Relative orbital x coordinate / radius (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Orbital phase nt (radian) −1.0 −0.5 0.0 0.5 1.0 Relative orbital x coordinate / radius (dimensionless) Circular two-body orbital coordinate Stated analytical example Worked point: (3.142, -1)
Orange point: horizontal coordinate 3.1416, calculated vertical coordinate -1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3.1416, into the displayed formula to obtain -1 on the vertical axis. Values are rounded for display.

Scope. Exact two-body circular orbit, not a general many-body solution; a one-coordinate time trace is shown.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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General relativity model · Example 1

Gravitational time dilation outside a sphere

Problem & parameters. For a stationary observer outside a nonrotating spherical mass, compare proper time with Schwarzschild coordinate time at infinity.

dτ/dt=1−rs/rd\tau/dt=\sqrt{1-r_s/r}

Solution. Set spatial coordinate increments to zero in the Schwarzschild line element and take the square root of its time coefficient.

General relativity model: Gravitational time dilation outside a sphere. Horizontal axis: Schwarzschild radius ratio r / rs (dimensionless). Vertical axis: Static clock rate dτ/dt (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 1 2 3 4 5 6 7 8 Schwarzschild radius ratio r / rs (dimensionless) 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Static clock rate dτ/dt (dimensionless) Gravitational time dilation outside a sphere Stated analytical example Worked point: (4.525, 0.8826)
Orange point: horizontal coordinate 4.525, calculated vertical coordinate 0.88261. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 4.525, into the displayed formula to obtain 0.88261 on the vertical axis. Values are rounded for display.

Scope. Exterior vacuum Schwarzschild solution, r>rs. A static observer cannot remain at the horizon.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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FLRW cosmological model · Example 1

Matter-dominated cosmic expansion

Problem & parameters. Take a spatially flat FLRW universe with pressureless matter only and zero cosmological constant.

a(t)/a(t∗)=(t/t∗)2/3a(t)/a(t_*)=(t/t_*)^{2/3}

Solution. Mass conservation gives ρ∝a^−3. Friedmann’s equation then gives ȧ∝a^−1/2; integrate from the big-bang branch.

FLRW cosmological model: Matter-dominated cosmic expansion. Horizontal axis: Cosmic time t / t* (dimensionless). Vertical axis: Scale factor a / a* (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Cosmic time t / t* (dimensionless) 0.0 0.5 1.0 1.5 2.0 Scale factor a / a* (dimensionless) Matter-dominated cosmic expansion Stated analytical example Worked point: (1.51, 1.316)
Orange point: horizontal coordinate 1.51, calculated vertical coordinate 1.3162. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.51, into the displayed formula to obtain 1.3162 on the vertical axis. Values are rounded for display.

Scope. Matter-only special case, not a fit to the present universe.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Smoothed particle hydrodynamics (SPH) · Example 1

Normalized SPH kernel section

Problem & parameters. Evaluate the standard one-dimensional cubic-spline smoothing kernel of support radius 2h.

hW(q)=23{1−1.5q2+0.75q3q<1(2−q)3/41≤q≤2hW(q)=\frac23\begin{cases}1-1.5q^2+0.75q^3&q<1\\(2-q)^3/4&1\le q\le2\end{cases}

Solution. Use q=|x|/h, apply the inner and outer polynomial branches, and normalize their integral to one.

Smoothed particle hydrodynamics (SPH): Normalized SPH kernel section. Horizontal axis: Kernel coordinate x / h (dimensionless). Vertical axis: Kernel weight hW (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 Kernel coordinate x / h (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Kernel weight hW (dimensionless) Normalized SPH kernel section Stated analytical example Worked point: (0, 0.6667)
Orange point: horizontal coordinate 0, calculated vertical coordinate 0.66667. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 0.66667 on the vertical axis. Values are rounded for display.

Scope. Kernel evaluation, not a complete SPH flow or solid simulation.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Discrete element method (DEM) · Example 1

Elastic DEM contact

Problem & parameters. Choose a linear frictionless normal-contact spring with stiffness k, no damping, and positive overlap.

F/(kδ∗)=δ/δ∗F/(k\delta_*)=\delta/\delta_*

Solution. The prescribed contact law is F=kδ. Before contact, F=0.

Discrete element method (DEM): Elastic DEM contact. Horizontal axis: Positive overlap δ / δ* (dimensionless). Vertical axis: Normal force / kδ* (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Positive overlap δ / δ* (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Normal force / kδ* (dimensionless) Elastic DEM contact Stated analytical example Worked point: (0.5, 0.5)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.5 on the vertical axis. Values are rounded for display.

Scope. One elastic contact contribution; many-particle dynamics and tangential friction are excluded.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Lattice Boltzmann method (LBM) · Example 1

Uniform LBM shear-mode decay target

Problem & parameters. Use a small-amplitude periodic transverse shear wave in the low-Mach hydrodynamic limit; plot νt/L²=0.02.

u(ξ,τ)=sin⁡(2πξ)e−4π2τ,τ=0.02u(\xi,\tau)=\sin(2\pi\xi)e^{-4\pi^2\tau},\quad\tau=0.02

Solution. The continuum transverse velocity obeys diffusion. Its wave number 2π/L fixes the exponential decay rate.

Lattice Boltzmann method (LBM): Uniform LBM shear-mode decay target. Horizontal axis: Periodic position x / L (dimensionless). Vertical axis: Transverse speed / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Periodic position x / L (dimensionless) −0.4 −0.2 0.0 0.2 0.4 Transverse speed / initial amplitude (dimensionless) Uniform LBM shear-mode decay target Stated analytical example Worked point: (0.5, 5.56e-17)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 5.5604e-17. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 5.5604e-17 on the vertical axis. Values are rounded for display.

Scope. Exact continuum benchmark for LBM, not a finite-lattice prediction; compressibility and lattice errors must be checked separately.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Material point method (MPM) · Example 1

Uniform bar extension benchmark

Problem & parameters. Apply a uniform small axial strain of 0.01 to a homogeneous elastic bar.

u(x)/L=0.01(x/L)u(x)/L=0.01(x/L)

Solution. Integrate du/dx=0.01 with u(0)=0.

Material point method (MPM): Uniform bar extension benchmark. Horizontal axis: Reference position x / L (dimensionless). Vertical axis: Displacement u / L (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Reference position x / L (dimensionless) 0.000 0.002 0.004 0.006 0.008 0.010 Displacement u / L (dimensionless) Uniform bar extension benchmark Stated analytical example Worked point: (0.5, 0.005)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.005. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.005 on the vertical axis. Values are rounded for display.

Scope. Exact continuum target for MPM; grid transfer and particle quadrature errors are not represented.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Homogenization · Example 1

Parallel-layer effective modulus

Problem & parameters. Two perfectly bonded parallel axial bars share the same strain, with modulus ratio E2/E1=4.

Eeff/E1=(1−f)+4fE_{eff}/E_1=(1-f)+4f

Solution. Average stress is [(1−f)E1+fE2] times the common strain. Divide by strain to obtain the effective axial modulus.

Homogenization: Parallel-layer effective modulus. Horizontal axis: Stiff-phase volume fraction f (dimensionless). Vertical axis: Effective modulus / soft modulus (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Stiff-phase volume fraction f (dimensionless) 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Effective modulus / soft modulus (dimensionless) Parallel-layer effective modulus Stated analytical example Worked point: (0.5, 2.5)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 2.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 2.5 on the vertical axis. Values are rounded for display.

Scope. Exact iso-strain parallel-bar construction; generally an upper-bound estimate for other microstructures.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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QM/MM coupling · Example 1

Coupled-region harmonic reference

Problem & parameters. As a consistency check, choose a common harmonic coordinate whose total coupled-region energy is kq²/2 and whose effective mass is m.

q/A=cos⁡(ωt)q/A=\cos(\omega t)

Solution. The total force is −kq. Solve m q″+kq=0 with q(0)=A and q′(0)=0.

QM/MM coupling: Coupled-region harmonic reference. Horizontal axis: Phase ωt (radian). Vertical axis: Coordinate / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Phase ωt (radian) −1.0 −0.5 0.0 0.5 1.0 Coordinate / initial amplitude (dimensionless) Coupled-region harmonic reference Stated analytical example Worked point: (3.142, -1)
Orange point: horizontal coordinate 3.1416, calculated vertical coordinate -1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3.1416, into the displayed formula to obtain -1 on the vertical axis. Values are rounded for display.

Scope. Prescribed harmonic reference only; no electronic calculation, interface force transfer, or adaptive region simulation is performed.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Atomistic–continuum coupling · Example 1

Coupled-region harmonic reference

Problem & parameters. As a consistency check, choose a common harmonic coordinate whose total coupled-region energy is kq²/2 and whose effective mass is m.

q/A=cos⁡(ωt)q/A=\cos(\omega t)

Solution. The total force is −kq. Solve m q″+kq=0 with q(0)=A and q′(0)=0.

Atomistic–continuum coupling: Coupled-region harmonic reference. Horizontal axis: Phase ωt (radian). Vertical axis: Coordinate / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Phase ωt (radian) −1.0 −0.5 0.0 0.5 1.0 Coordinate / initial amplitude (dimensionless) Coupled-region harmonic reference Stated analytical example Worked point: (3.142, -1)
Orange point: horizontal coordinate 3.1416, calculated vertical coordinate -1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3.1416, into the displayed formula to obtain -1 on the vertical axis. Values are rounded for display.

Scope. Prescribed harmonic reference only; no electronic calculation, interface force transfer, or adaptive region simulation is performed.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Fluid–structure interaction (FSI) · Example 1

Added-mass structural oscillation

Problem & parameters. Approximate fluid loading as a constant added mass ma=m on an undamped spring-supported body.

q/A=cos⁡(τ1+ma/m),ma/m=1q/A=\cos\left(\frac{\tau}{\sqrt{1+m_a/m}}\right),\quad m_a/m=1

Solution. Combine the masses: (m+ma)q″+kq=0. The frequency becomes √[k/(m+ma)].

Fluid–structure interaction (FSI): Added-mass structural oscillation. Horizontal axis: Dry structural phase τ = √(k/m)t (radian). Vertical axis: Displacement / initial amplitude (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 2 4 6 8 10 12 Dry structural phase τ = √(k/m)t (radian) −1.0 −0.5 0.0 0.5 1.0 Displacement / initial amplitude (dimensionless) Added-mass structural oscillation Stated analytical example Worked point: (6.283, -0.2663)
Orange point: horizontal coordinate 6.2832, calculated vertical coordinate -0.26626. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 6.2832, into the displayed formula to obtain -0.26626 on the vertical axis. Values are rounded for display.

Scope. Linear added-mass reduction of FSI; no viscous drag, free-surface, or flow-field solution.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Thermomechanical coupling · Example 1

Fully restrained thermal expansion

Problem & parameters. A one-dimensional elastic bar is prevented from expanding while its temperature rises uniformly.

σ/(EαΔT∗)=−ΔT/ΔT∗\sigma/(E\alpha\Delta T_*)=-\Delta T/\Delta T_*

Solution. Total strain is σ/E+αΔT. Set it to zero and solve for stress.

Thermomechanical coupling: Fully restrained thermal expansion. Horizontal axis: Temperature rise / reference rise (dimensionless). Vertical axis: Scaled axial stress (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 Temperature rise / reference rise (dimensionless) −2.0 −1.5 −1.0 −0.5 0.0 Scaled axial stress (dimensionless) Fully restrained thermal expansion Stated analytical example Worked point: (1, -1)
Orange point: horizontal coordinate 1, calculated vertical coordinate -1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1, into the displayed formula to obtain -1 on the vertical axis. Values are rounded for display.

Scope. Small-strain constant-property axial model, with tension positive; uniform heating produces compression.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Proper orthogonal decomposition (POD) · Example 1

Exactly rank-one snapshot reconstruction

Problem & parameters. All snapshots are scalar multiples of sin(πx). Reconstruct the snapshot at dimensionless time one using one POD mode.

u(x,t)=e−tsin⁡(πx),t=1u(x,t)=e^{-t}\sin(\pi x),\quad t=1

Solution. The snapshot matrix has rank one. Its only nonzero spatial mode is proportional to sin(πx), with coefficient exp(−t).

Proper orthogonal decomposition (POD): Exactly rank-one snapshot reconstruction. Horizontal axis: Position x / L (dimensionless). Vertical axis: Reconstructed field (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Position x / L (dimensionless) 0.0 0.1 0.2 0.3 0.4 Reconstructed field (dimensionless) Exactly rank-one snapshot reconstruction Stated analytical example Worked point: (0.5, 0.3679)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.36788. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.36788 on the vertical axis. Values are rounded for display.

Scope. Exact rank-one constructed data set; real POD truncation can incur substantial error.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Gaussian-process surrogate · Example 1

One-observation Gaussian-process posterior mean

Problem & parameters. Use a zero-mean, unit-variance squared-exponential GP, unit length scale, and one noiseless observation y(0)=1.

μ(x)=e−x2/2\mu(x)=e^{-x^2/2}

Solution. The one-by-one training covariance is one. The conditional mean k(x,0)K^−1y equals exp(−x²/2).

Gaussian-process surrogate: One-observation Gaussian-process posterior mean. Horizontal axis: Input / kernel length scale (dimensionless). Vertical axis: Posterior mean (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −3 −2 −1 0 1 2 3 Input / kernel length scale (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Posterior mean (dimensionless) One-observation Gaussian-process posterior mean Stated analytical example Worked point: (0, 1)
Orange point: horizontal coordinate 0, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Analytical posterior mean under the stated kernel; it is not a physical law, and posterior uncertainty is not shown.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Digital twin framework · Example 1

Digital-twin reference cooling trajectory

Problem & parameters. Use a lumped thermal model with a known constant cooling time as an ideal reference for a thermal digital twin.

(T−T∞)/(T0−T∞)=e−t/τ(T-T_\infty)/(T_0-T_\infty)=e^{-t/\tau}

Solution. Solve the first-order heat balance analytically; compare actual sensor data with this reference in a real implementation.

Digital twin framework: Digital-twin reference cooling trajectory. Horizontal axis: Elapsed time / thermal constant (dimensionless). Vertical axis: Temperature excess / initial excess (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Elapsed time / thermal constant (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Temperature excess / initial excess (dimensionless) Digital-twin reference cooling trajectory Stated analytical example Worked point: (2.5, 0.08208)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 0.082085. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 0.082085 on the vertical axis. Values are rounded for display.

Scope. Reference physics only. No sensors, online updates, or actual equipment measurements are included.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Bayesian model calibration · Example 1

Gaussian conjugate posterior density

Problem & parameters. Use prior θ~Normal(0,1) and one measurement y=1 with independent Normal(0,1) measurement noise.

p(θ∣y=1)=π−1/2e−(θ−0.5)2p(\theta\mid y=1)=\pi^{-1/2}e^{-(\theta-0.5)^2}

Solution. Add prior and data precisions to obtain variance 1/2; precision-weight the means to obtain posterior mean 1/2. Normalize the Gaussian.

Bayesian model calibration: Gaussian conjugate posterior density. Horizontal axis: Unknown parameter θ (dimensionless). Vertical axis: Posterior density (per unit θ). image/svg+xml IICSM analytical illustration / Matplotlib −2 −1 0 1 2 3 Unknown parameter θ (dimensionless) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Posterior density (per unit θ) Gaussian conjugate posterior density Stated analytical example Worked point: (0.5, 0.5642)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.56419. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.56419 on the vertical axis. Values are rounded for display.

Scope. Exact conjugate scalar calibration example; not a calibrated engineering system.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Polynomial chaos expansion · Example 1

First-order polynomial chaos response

Problem & parameters. Use a linear response to a uniform random input. Expand in the first two Legendre polynomials.

Y(ξ)=2+0.5ξ,ξ∼U[−1,1]Y(\xi)=2+0.5\xi,\quad\xi\sim U[-1,1]

Solution. Since P0=1 and P1=ξ, coefficients are 2 and 0.5. The mean is 2 and variance is 0.25/3.

Polynomial chaos expansion: First-order polynomial chaos response. Horizontal axis: Uniform random input ξ (dimensionless). Vertical axis: Response Y (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Uniform random input ξ (dimensionless) 1.4 1.6 1.8 2.0 2.2 2.4 2.6 Response Y (dimensionless) First-order polynomial chaos response Stated analytical example Worked point: (0, 2)
Orange point: horizontal coordinate 0, calculated vertical coordinate 2. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0, into the displayed formula to obtain 2 on the vertical axis. Values are rounded for display.

Scope. Exact degree-one expansion for the chosen response, not a surrogate fitted to arbitrary simulation data.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Geometrically scaled physical model · Example 1

Geometric volume scaling

Problem & parameters. Scale all dimensions of a shape by the same positive length ratio.

Vm/Vp=(Lm/Lp)3V_m/V_p=(L_m/L_p)^3

Solution. Volume is the product of three lengths; multiply the three identical scale factors.

Geometrically scaled physical model: Geometric volume scaling. Horizontal axis: Model / prototype length (dimensionless). Vertical axis: Model / prototype volume (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Model / prototype length (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Model / prototype volume (dimensionless) Geometric volume scaling Stated analytical example Worked point: (0.5, 0.125)
Orange point: horizontal coordinate 0.5, calculated vertical coordinate 0.125. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.5, into the displayed formula to obtain 0.125 on the vertical axis. Values are rounded for display.

Scope. Geometric similarity alone does not ensure force, material, or dynamic similarity.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Wind-tunnel model · Example 1

Dynamic pressure in a wind-tunnel test

Problem & parameters. Use fixed air density and reference dynamic pressure q*=ρU*²/2.

q/q∗=(U/U∗)2q/q_*=(U/U_*)^2

Solution. Evaluate q=ρU²/2 and divide by q*.

Wind-tunnel model: Dynamic pressure in a wind-tunnel test. Horizontal axis: Tunnel speed / reference speed (dimensionless). Vertical axis: Dynamic pressure / reference pressure (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 Tunnel speed / reference speed (dimensionless) 0 1 2 3 4 Dynamic pressure / reference pressure (dimensionless) Dynamic pressure in a wind-tunnel test Stated analytical example Worked point: (1, 1)
Orange point: horizontal coordinate 1, calculated vertical coordinate 1. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1, into the displayed formula to obtain 1 on the vertical axis. Values are rounded for display.

Scope. Test-planning relation, not measured wind-tunnel data; Reynolds and Mach similarity require separate checks.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Hydraulic flume model · Example 1

Froude-similar velocity scaling

Problem & parameters. Use the same gravitational acceleration and match Froude number U/√(gL) between a model and prototype.

Um/Up=Lm/LpU_m/U_p=\sqrt{L_m/L_p}

Solution. Equate the two Froude numbers and solve for the velocity ratio.

Hydraulic flume model: Froude-similar velocity scaling. Horizontal axis: Model / prototype length (dimensionless). Vertical axis: Model / prototype speed (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Model / prototype length (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Model / prototype speed (dimensionless) Froude-similar velocity scaling Stated analytical example Worked point: (0.505, 0.7106)
Orange point: horizontal coordinate 0.505, calculated vertical coordinate 0.71063. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.505, into the displayed formula to obtain 0.71063 on the vertical axis. Values are rounded for display.

Scope. Gravity-dominated similarity appropriate to free-surface flumes; Reynolds, Weber, and other dimensionless groups may not also match.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Dimensional-analysis similarity model · Example 1

Froude-similar velocity scaling

Problem & parameters. Use the same gravitational acceleration and match Froude number U/√(gL) between a model and prototype.

Um/Up=Lm/LpU_m/U_p=\sqrt{L_m/L_p}

Solution. Equate the two Froude numbers and solve for the velocity ratio.

Dimensional-analysis similarity model: Froude-similar velocity scaling. Horizontal axis: Model / prototype length (dimensionless). Vertical axis: Model / prototype speed (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.2 0.4 0.6 0.8 1.0 Model / prototype length (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Model / prototype speed (dimensionless) Froude-similar velocity scaling Stated analytical example Worked point: (0.505, 0.7106)
Orange point: horizontal coordinate 0.505, calculated vertical coordinate 0.71063. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.505, into the displayed formula to obtain 0.71063 on the vertical axis. Values are rounded for display.

Scope. Gravity-dominated similarity appropriate to free-surface flumes; Reynolds, Weber, and other dimensionless groups may not also match.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Shake-table structural model · Example 1

Undamped shake-table reference transfer

Problem & parameters. For an undamped single-degree-of-freedom oscillator with sinusoidal base motion, calculate the steady absolute displacement below resonance.

∣X/Y∣=1/∣1−r2∣,r=Ω/ωn|X/Y|=1/|1-r^2|,\quad r=\Omega/\omega_n

Solution. Insert harmonic motions into mX″+k(X−Y)=0. Solve (k−mΩ²)X=kY for the amplitude ratio.

Shake-table structural model: Undamped shake-table reference transfer. Horizontal axis: Excitation / natural frequency (dimensionless). Vertical axis: Absolute displacement amplitude ratio (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Excitation / natural frequency (dimensionless) 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 Absolute displacement amplitude ratio (dimensionless) Undamped shake-table reference transfer Stated analytical example Worked point: (0.4, 1.19)
Orange point: horizontal coordinate 0.4, calculated vertical coordinate 1.1905. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.4, into the displayed formula to obtain 1.1905 on the vertical axis. Values are rounded for display.

Scope. Ideal steady reference, not shake-table measurements; the undamped resonance singularity is outside the plotted range.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Photoelastic model · Example 1

Photoelastic fringe order

Problem & parameters. Use a transparent specimen of thickness t, stress-optic coefficient C, and monochromatic wavelength λ.

N=Ct(σ1−σ2)/λN=Ct(\sigma_1-\sigma_2)/\lambda

Solution. The principal refractive-index difference is CΔσ. Optical path retardation is CtΔσ; divide by wavelength to obtain fringe order.

Photoelastic model: Photoelastic fringe order. Horizontal axis: Stress–optic retardation CtΔσ / λ (dimensionless). Vertical axis: Fringe order N (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 Stress–optic retardation CtΔσ / λ (dimensionless) 0 1 2 3 4 5 Fringe order N (dimensionless) Photoelastic fringe order Stated analytical example Worked point: (2.5, 2.5)
Orange point: horizontal coordinate 2.5, calculated vertical coordinate 2.5. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 2.5, into the displayed formula to obtain 2.5 on the vertical axis. Values are rounded for display.

Scope. Uniform stress through thickness and linear stress-optic law; this is not a fringe photograph.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Ornstein-Zernike equation · Example 1

OZ structure factor with prescribed direct correlation

Problem & parameters. Assume rho times the Fourier-transformed direct correlation is −exp[−(kℓ)²]. Find S(k) from the OZ relation.

ρc^(k)=−e−(kℓ)2,S(k)=11+e−(kℓ)2\rho\widehat c(k)=-e^{-(k\ell)^2},\quad S(k)=\frac{1}{1+e^{-(k\ell)^2}}

Solution. Fourier transformation gives h_hat=c_hat/(1−rho c_hat). Substitute into S=1+rho h_hat to obtain the displayed expression. At k=0, S=1/2; at large k, S tends to 1.

Ornstein-Zernike equation: OZ structure factor with prescribed direct correlation. Horizontal axis: Wavevector magnitude kℓ (dimensionless). Vertical axis: Structure factor S(k) (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Wavevector magnitude kℓ (dimensionless) 0.5 0.6 0.7 0.8 0.9 1.0 Structure factor S(k) (dimensionless) OZ structure factor with prescribed direct correlation Stated analytical example Worked point: (1.5, 0.9047)
Orange point: horizontal coordinate 1.5, calculated vertical coordinate 0.90465. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5, into the displayed formula to obtain 0.90465 on the vertical axis. Values are rounded for display.

Scope. A prescribed-correlation algebraic benchmark, not a self-consistent closure solution or measured scattering spectrum.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Percus-Yevick closure · Example 1

Percus-Yevick hard-sphere contact value

Problem & parameters. Use the analytical three-dimensional, monodisperse hard-sphere PY solution to evaluate its contact pair distribution as packing fraction varies.

g(σ+)=1+ϕ/2(1−ϕ)2g(\sigma^+)=\frac{1+\phi/2}{(1-\phi)^2}

Solution. The PY hard-sphere solution gives virial-route Z=(1+2φ+3φ²)/(1−φ)². The hard-sphere contact theorem Z=1+4φg(σ+) then gives g(σ+)=(1+φ/2)/(1−φ)² after subtraction and cancellation. At φ=0 use the limit g=1.

Percus-Yevick closure: Percus-Yevick hard-sphere contact value. Horizontal axis: Hard-sphere packing fraction φ (dimensionless). Vertical axis: Contact pair distribution g(σ+) (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.1 0.2 0.3 0.4 Hard-sphere packing fraction φ (dimensionless) 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Contact pair distribution g(σ+) (dimensionless) Percus-Yevick hard-sphere contact value Stated analytical example Worked point: (0.225, 1.852)
Orange point: horizontal coordinate 0.225, calculated vertical coordinate 1.8522. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.225, into the displayed formula to obtain 1.8522 on the vertical axis. Values are rounded for display.

Scope. Contact-value evaluation of the PY approximation; the analytical OZ/PY solution is taken as the starting result. Thermodynamic routes are not identical.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Hypernetted-chain (HNC) closure · Example 1

Dilute HNC Gaussian-core pair distribution

Problem & parameters. Take the zero-density limit of an equilibrium soft Gaussian-core fluid with beta epsilon=1. Find its pair distribution.

βu(r)=e−(r/σ)2,g(r)=exp⁡[−e−(r/σ)2]\beta u(r)=e^{-(r/\sigma)^2},\quad g(r)=\exp[-e^{-(r/\sigma)^2}]

Solution. As density tends to zero, OZ gives h=c and hence gamma=0. HNC reduces to the two-particle Boltzmann factor exp(−beta u); insert the specified Gaussian repulsion. At r=0, g=exp(−1).

Hypernetted-chain (HNC) closure: Dilute HNC Gaussian-core pair distribution. Horizontal axis: Separation r / σ (dimensionless). Vertical axis: Pair distribution g(r) (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Separation r / σ (dimensionless) 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Pair distribution g(r) (dimensionless) Dilute HNC Gaussian-core pair distribution Stated analytical example Worked point: (1.5, 0.9)
Orange point: horizontal coordinate 1.5, calculated vertical coordinate 0.89997. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5, into the displayed formula to obtain 0.89997 on the vertical axis. Values are rounded for display.

Scope. Exact dilute two-particle limit for this specified potential; at finite liquid density, solve the coupled HNC/OZ equations instead.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Carnahan-Starling hard-sphere equation of state · Example 1

Hard-sphere pressure amplification

Problem & parameters. For a monodisperse hard-sphere fluid, compute pressure relative to ideal-gas pressure from packing fraction using Carnahan-Starling.

Z(ϕ)=1+ϕ+ϕ2−ϕ3(1−ϕ)3Z(\phi)=\frac{1+\phi+\phi^2-\phi^3}{(1-\phi)^3}

Solution. Insert φ into the numerator and denominator. For example, at φ=0.3 the numerator is 1.363 and denominator 0.343, giving Z=3.97376. The dilute limit is Z=1.

Carnahan-Starling hard-sphere equation of state: Hard-sphere pressure amplification. Horizontal axis: Hard-sphere packing fraction φ (dimensionless). Vertical axis: Compressibility factor Z = p / (ρkBT) (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.1 0.2 0.3 0.4 Hard-sphere packing fraction φ (dimensionless) 0 2 4 6 8 10 Compressibility factor Z = p / (ρkBT) (dimensionless) Hard-sphere pressure amplification Stated analytical example Worked point: (0.225, 2.716)
Orange point: horizontal coordinate 0.225, calculated vertical coordinate 2.716. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.225, into the displayed formula to obtain 2.716 on the vertical axis. Values are rounded for display.

Scope. Constitutive evaluation of the approximate fluid EOS; no attractive forces, mixture effects, or solid phase are included.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Stokes-Einstein diffusion relation · Example 1

Brownian sphere diffusion in a viscous solvent

Problem & parameters. Take T=298 K and solvent viscosity eta=0.001 Pa s. Estimate D for dilute no-slip spherical probes with radii between 10 and 200 nm.

D(R)=(1.380649×10−23)(298)6π(10−3)(R×10−9)  m2 s−1D(R)=\frac{(1.380649\times10^{-23})(298)}{6\pi(10^{-3})(R\times10^{-9})}\;\mathrm{m^2\,s^{-1}}

Solution. Convert radius from nm to m and substitute into Stokes-Einstein. At R=100 nm, D=2.18273×10⁻¹² m²/s. Doubling radius halves diffusivity.

Stokes-Einstein diffusion relation: Brownian sphere diffusion in a viscous solvent. Horizontal axis: Hydrodynamic radius R (nm). Vertical axis: Translational diffusivity D (m²/s). image/svg+xml IICSM analytical illustration / Matplotlib 25 50 75 100 125 150 175 200 Hydrodynamic radius R (nm) 0.0 0.5 1.0 1.5 2.0 Translational diffusivity D (m²/s) 1e−11 Brownian sphere diffusion in a viscous solvent Stated analytical example Worked point: (105, 2.079e-12)
Orange point: horizontal coordinate 105, calculated vertical coordinate 2.0788e-12. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 105, into the displayed formula to obtain 2.0788e-12 on the vertical axis. Values are rounded for display.

Scope. Chosen constant solvent viscosity, not a measured water-property curve. Continuum, no-slip, dilute-sphere assumptions apply.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Green-Kubo viscosity relation · Example 1

Viscosity integral for an exponential stress correlation

Problem & parameters. Assume the equilibrium intensive shear-pressure autocorrelation C(t)=C0 exp(−t/τ), with C0>0. Calculate the running Green-Kubo viscosity integral.

C(t)=C0e−t/τ,η(t)η∞=1−e−t/τ,η∞=VC0τkBTC(t)=C_0e^{-t/\tau},\quad\frac{\eta(t)}{\eta_\infty}=1-e^{-t/\tau},\quad\eta_\infty=\frac{VC_0\tau}{k_{\mathrm B}T}

Solution. Integrate C0 exp(−s/τ) from 0 to t to obtain C0τ[1−exp(−t/τ)]. Multiply by V/(kBT) and divide by its infinite-time limit. At t=3τ, 95.0213% of the assumed total is recovered.

Green-Kubo viscosity relation: Viscosity integral for an exponential stress correlation. Horizontal axis: Integration time t / τ (dimensionless). Vertical axis: Running viscosity η(t) / η∞ (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0 1 2 3 4 5 6 Integration time t / τ (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Running viscosity η(t) / η∞ (dimensionless) Viscosity integral for an exponential stress correlation Stated analytical example Worked point: (3, 0.9502)
Orange point: horizontal coordinate 3, calculated vertical coordinate 0.95021. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 3, into the displayed formula to obtain 0.95021 on the vertical axis. Values are rounded for display.

Scope. Analytical exponential-correlation benchmark; real liquid stress correlations may oscillate or have long tails. This is not a molecular-dynamics measurement.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Einstein crystal heat-capacity model · Example 1

Einstein oscillator heat capacity

Problem & parameters. For 3N identical oscillators, calculate the normalized heat capacity versus temperature.

θ=T/ΘE,CV3NkB=θ−2e−1/θ(1−e−1/θ)2\theta=T/\Theta_{\mathrm E},\quad\frac{C_V}{3Nk_{\mathrm B}}=\frac{\theta^{-2}e^{-1/\theta}}{(1-e^{-1/\theta})^2}

Solution. Each oscillator has thermal energy hbar omega/[exp(hbar omega/kBT)−1]. Differentiate and divide the total by 3NkB. At T=ThetaE, the result is e/(e−1)²=0.920674.

Einstein crystal heat-capacity model: Einstein oscillator heat capacity. Horizontal axis: Temperature T / ΘE (dimensionless). Vertical axis: Heat capacity CV / (3NkB) (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 Temperature T / ΘE (dimensionless) 0.0 0.2 0.4 0.6 0.8 1.0 Heat capacity CV / (3NkB) (dimensionless) Einstein oscillator heat capacity Stated analytical example Worked point: (1.05, 0.9277)
Orange point: horizontal coordinate 1.05, calculated vertical coordinate 0.92772. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.05, into the displayed formula to obtain 0.92772 on the vertical axis. Values are rounded for display.

Scope. Exact evaluation within the single-frequency harmonic Einstein model; the acoustic low-temperature cubic law is absent.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Debye phonon model · Example 1

Debye low-temperature cubic law

Problem & parameters. In the regime T much smaller than ThetaD, estimate lattice heat capacity using the leading Debye asymptote.

CVNkB≃12π45(TΘD)3\frac{C_V}{Nk_{\mathrm B}}\simeq\frac{12\pi^4}{5}\left(\frac{T}{\Theta_{\mathrm D}}\right)^3

Solution. Extend the Debye integral upper limit to infinity. Its value is 4pi⁴/15, so multiplying by 9(T/ThetaD)³ gives 12pi⁴(T/ThetaD)³/5. Doubling temperature multiplies this leading term by eight.

Debye phonon model: Debye low-temperature cubic law. Horizontal axis: Temperature T / ΘD (dimensionless). Vertical axis: Lattice heat capacity CV / (NkB) (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.01 0.02 0.03 0.04 0.05 Temperature T / ΘD (dimensionless) 0.000 0.005 0.010 0.015 0.020 0.025 0.030 Lattice heat capacity CV / (NkB) (dimensionless) Debye low-temperature cubic law Stated analytical example Worked point: (0.0275, 0.004862)
Orange point: horizontal coordinate 0.0275, calculated vertical coordinate 0.0048619. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.0275, into the displayed formula to obtain 0.0048619 on the vertical axis. Values are rounded for display.

Scope. Low-temperature analytical asymptote only; the plotted range stops at T/ThetaD=0.05. Use the finite-cutoff integral outside this regime.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Sommerfeld free-electron model · Example 1

Sommerfeld electronic heat capacity

Problem & parameters. Find the leading electronic heat capacity of a three-dimensional free-electron gas at fixed electron number and low temperature.

CeNkB≃π22TTF\frac{C_e}{Nk_{\mathrm B}}\simeq\frac{\pi^2}{2}\frac{T}{T_{\mathrm F}}

Solution. The fixed-number Sommerfeld expansion gives U/N=(3/5)EF+(pi²/4)(kBT)²/EF to this order. Differentiate with respect to temperature, and use EF=kB TF. At T/TF=0.01, Ce/(NkB)=0.0493480.

Sommerfeld free-electron model: Sommerfeld electronic heat capacity. Horizontal axis: Temperature T / TF (dimensionless). Vertical axis: Electronic heat capacity Ce / (NkB) (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.00 0.01 0.02 0.03 0.04 0.05 Temperature T / TF (dimensionless) 0.00 0.05 0.10 0.15 0.20 0.25 Electronic heat capacity Ce / (NkB) (dimensionless) Sommerfeld electronic heat capacity Stated analytical example Worked point: (0.0255, 0.1258)
Orange point: horizontal coordinate 0.0255, calculated vertical coordinate 0.12584. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.0255, into the displayed formula to obtain 0.12584 on the vertical axis. Values are rounded for display.

Scope. Leading low-temperature contribution of ideal electrons only; excludes lattice heat capacity, band corrections, interactions, and superconductivity.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Tight-binding electronic model · Example 1

Nearest-neighbor tight-binding band

Problem & parameters. Use a one-dimensional chain with one orbital per site and positive nearest-neighbor hopping t. Find its band over half the Brillouin zone.

E(k)−ϵ0t=−2cos⁡(ka)\frac{E(k)-\epsilon_0}{t}=-2\cos(ka)

Solution. Insert amplitudes c_n=exp(ikna) into E c_n=epsilon0 c_n−t(c_(n+1)+c_(n−1)). Divide by c_n and combine the two exponentials as 2cos(ka). The band runs from epsilon0−2t to epsilon0+2t.

Tight-binding electronic model: Nearest-neighbor tight-binding band. Horizontal axis: Crystal wavevector ka (radian). Vertical axis: Band energy (E − ε0) / t (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Crystal wavevector ka (radian) −2 −1 0 1 2 Band energy (E − ε0) / t (dimensionless) Nearest-neighbor tight-binding band Stated analytical example Worked point: (1.571, -1.225e-16)
Orange point: horizontal coordinate 1.5708, calculated vertical coordinate -1.2246e-16. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5708, into the displayed formula to obtain -1.2246e-16 on the vertical axis. Values are rounded for display.

Scope. One-orbital, orthonormal, noninteracting chain. The other half-zone follows by inversion symmetry; real semiconductor bands generally need multiple orbitals.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Nearly-free-electron model · Example 1

Nearly-free-electron avoided crossing

Problem & parameters. Let ER=hbar²(G/2)²/(2m) and a real lattice Fourier coupling VG=0.1 ER. Calculate the lower branch near k=G/2.

HER=((1+q)20.10.1(q−1)2),E−ER=1+q2−4q2+0.01\frac{H}{E_R}=\begin{pmatrix}(1+q)^2&0.1\\0.1&(q-1)^2\end{pmatrix},\quad\frac{E_-}{E_R}=1+q^2-\sqrt{4q^2+0.01}

Solution. The two free plane-wave energies are ER(1+q)² and ER(q−1)². Diagonalizing their 2 by 2 matrix gives the displayed lower eigenvalue. At q=0 the energies are 0.9 ER and 1.1 ER, separated by 0.2 ER.

Nearly-free-electron model: Nearly-free-electron avoided crossing. Horizontal axis: Offset q = 2k/G − 1 (dimensionless). Vertical axis: Lower band energy E− / ER (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.00 0.05 0.10 0.15 0.20 0.25 0.30 Offset q = 2k/G − 1 (dimensionless) 0.5 0.6 0.7 0.8 0.9 Lower band energy E− / ER (dimensionless) Nearly-free-electron avoided crossing Stated analytical example Worked point: (0.15, 0.7063)
Orange point: horizontal coordinate 0.15, calculated vertical coordinate 0.70627. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 0.15, into the displayed formula to obtain 0.70627 on the vertical axis. Values are rounded for display.

Scope. Exact two-state diagonalization, approximate nearly-free-electron physics. Only the lower branch on one side of the Bragg plane is plotted; remote plane waves are omitted.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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Harmonic lattice dynamics · Example 1

Monatomic harmonic-chain dispersion

Problem & parameters. Take identical masses m separated by a, joined by nearest-neighbor springs K. Find the normal-mode dispersion over half the Brillouin zone.

ω(q)2K/m=sin⁡qa2(0≤qa≤π)\frac{\omega(q)}{2\sqrt{K/m}}=\sin\frac{qa}{2}\quad(0\le qa\le\pi)

Solution. Insert u_n=A exp[i(qna−omega t)] into m u_n″=K(u_(n+1)+u_(n−1)−2u_n). This gives omega²=(4K/m)sin²(qa/2). Select the nonnegative frequency. At small q the sound speed is a sqrt(K/m).

Harmonic lattice dynamics: Monatomic harmonic-chain dispersion. Horizontal axis: Phonon wavevector qa (radian). Vertical axis: Frequency ω / [2√(K/m)] (dimensionless). image/svg+xml IICSM analytical illustration / Matplotlib 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Phonon wavevector qa (radian) 0.0 0.2 0.4 0.6 0.8 1.0 Frequency ω / [2√(K/m)] (dimensionless) Monatomic harmonic-chain dispersion Stated analytical example Worked point: (1.571, 0.7071)
Orange point: horizontal coordinate 1.5708, calculated vertical coordinate 0.70711. Axis labels specify the quantities and units or normalization.

Worked evaluation. Substitute the marked horizontal coordinate, 1.5708, into the displayed formula to obtain 0.70711 on the vertical axis. Values are rounded for display.

Scope. One-dimensional harmonic monatomic chain; no optical branch, anharmonic scattering, or measured material parameters.

Use the model entry’s references and assumptions for the full formulation. This curve is an analytical illustration, not measured product data.

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