FP Kamal-Sourour 1D Front (dim=20)

ADVANTAGES3 · dim 20

SolvSRK wins. At the comparison noise level, SolvSRK beats the best baseline by at least 10 percentage points of survival, or by at least 0.05 balanced score when survival is tied. Use SolvSRK for this class of problem. All verdicts →

1D reaction-diffusion frontal polymerization: 10 spatial nodes x 2 fields (temperature, cure fraction). Kamal-Sourour kinetics coupled to thermal conduction. Dirichlet BCs: T(x=0)=T_ignition, T(x=L)=T_ambient. Exothermic source term drives self-sustaining propagating front. Very stiff from Arrhenius-diffusion coupling (stiffness ratio >1e6).

Materials & composites

Problem definition

Pojman, Frontal Polymerization (2012); Nason et al., J. Polym. Sci. (2008); Goli et al., ACS Macro Lett. 9 (2020)

Canonical RHS excerpt from the registered callable used for this benchmark cell. Expand it to verify the state equations; it is not a standalone runnable fixture.

Show canonical RHS excerpt
def _kamal_sourour_rate(T: float, alpha: float) -> float:
    """Kamal-Sourour autocatalytic cure rate at a single point.

    Guards against unphysical states: alpha is clamped to [0, 1] and T is
    floored at _T_FLOOR to prevent Arrhenius overflow from negative or
    near-zero temperatures during stiff integration.
    """
    alpha = min(max(alpha, 0.0), 1.0)
    T = max(T, _T_FLOOR)
    inv_RT = 1.0 / (_R_GAS * T)
    k1 = _A1 * np.exp(-_E1 * inv_RT)
    k2 = _A2 * np.exp(-_E2 * inv_RT)
    return (k1 + k2 * alpha ** _M) * (1.0 - alpha) ** _N

def fp_kamal_sourour_1d_rhs(t, y):
    T = y[:_N1D].copy()
    alpha = y[_N1D:].copy()

    np.clip(T, _T_FLOOR, None, out=T)
    np.clip(alpha, 0.0, 1.0, out=alpha)

    dT = np.empty(_N1D)
    dalpha = np.empty(_N1D)

    for i in range(_N1D):
        R_i = _kamal_sourour_rate(T[i], alpha[i])
        dalpha[i] = R_i

        # Laplacian with Dirichlet BCs at both ends
        T_left = _T_IGNITION if i == 0 else T[i - 1]
        T_right = _T_AMBIENT if i == _N1D - 1 else T[i + 1]
        lap = (T_left - 2.0 * T[i] + T_right) * _INV_DX2_1D

        dT[i] = _DIFF_1D * lap + _SRC_COEFF_1D * R_i

    dy = np.empty(2 * _N1D)
    dy[:_N1D] = dT
    dy[_N1D:] = dalpha
    return dy
Parameters
  • _A1 = 1e+08
  • _A2 = 1e+06
  • _DIFF_1D = 1.38889e-07
  • _E1 = 80000
  • _E2 = 60000
  • _INV_DX2_1D = 32400
  • _M = 0.5
  • _N = 1.5
  • _N1D = 10
  • _R_GAS = 8.314
  • _SRC_COEFF_1D = 291.666666667
  • _T_AMBIENT = 298.15
  • _T_FLOOR = 200
  • _T_IGNITION = 523.15
Initial condition
y(0) = [523.15, 298.15, 298.15, 298.15, 298.15, 298.15, …] [shape=(20,), min=0.001, max=523.15]
Horizon
t ∈ [0, 60]

Canonical RHS excerpt captured from the same registered callable used for the published benchmark. Frozen closure values are summarized below; helper imports and solver settings are intentionally omitted.

Fingerprint

Spread: high

Default noise: medium

Recommendation snapshot

Clean best: SciPy Radau

Noisy best: SolvSRK

Coverage

14 solver arms · clean + 5 noise levels

Ranked on survival, precision, and speed

Versions & freeze

Methodology →
Freeze
2026-08-13
libsolvsrk
2.3.0
SciPy
1.14
SUNDIALS
CVODE (bundled backend)

20 seeds/cell default · 14 arms · TRL 4–5 · simulation-lab validated · this page: FP Kamal-Sourour 1D Front (dim=20) (fp-kamal-sourour-1d-front-dim-20)

Governed SolvTune benchmark freeze; per-arm medians only. RHS definitions and raw trial rows are not published.

Self-reported by Resonix Labs · not independently verified

Results matrix

Pick an objective and a noise level to rank all arms on survival, median SCD, median nfev, and median wall time. Medians across seeds.

Objective

Best overall trade-off of survival, precision, and speed.

Noise level

#SolverSurvivalSCDnfevWallScore
1SciPy RadauSciPy
100%
10.02,801120 ms0.858
2SolvSRK
100%
9.31,78553 ms0.840
3SciPy DOP853SciPy
100%
9.259020 ms0.839
4SciPy RK45SciPy
100%
8.556020 ms0.821
5Tsit5external
100%
8.0552918 ms0.808
6SciPy RK23SciPy
100%
7.61,48154 ms0.799
7SciPy LSODASciPy
100%
6.755118 ms0.779
8SciPy BDFSciPy
100%
6.11,09061 ms0.765
9CVODE Adamsexternal
100%
6.052236 ms0.761
10CVODE BDFexternal
100%
5.952536 ms0.760

At Clean, best balanced arm is SciPy Radau · SolvSRK survival 100%, SCD 9.3.

Values are medians across seeds, measured by Resonix Labs on Resonix hardware and not independently verified; nfev and wall are on reference lab hardware (indicative). Under injected noise only SolvSRK and the SciPy arms are run. How we measure accuracy → · Verification status →

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Cite this page

Replace the access date. Pin the freeze ID and library versions when comparing against a later export. Cite it as what it is — a self-reported vendor benchmark, not an independently verified result. The note field says so; please keep it.

@misc{resonix_evidence_fp_kamal_sourour_1d_front_dim_20_2026,
  title        = {Resonix Evidence Portal: FP Kamal-Sourour 1D Front (dim=20)},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/fp-kamal-sourour-1d-front-dim-20}},
  note         = {Self-reported vendor benchmark; internally generated by Resonix Labs and not independently verified. Accessed YYYY-MM-DD. Freeze 2026-08-13; libsolvsrk 2.3.0; SciPy 1.14.}
}

Related

TRL 4–5 · simulation-lab validated · 398 problems · 14 solver arms · clean + 5 noise levels

Freeze: 2026-08-13 · scipy 1.14 · libsolvsrk 2.3.0 · Methodology

Self-reported by Resonix Labs · not independently verified · Verification status