Partial / unstable. SolvSRK survives more than 0% but less than 90% of runs at the comparison noise level, without being beaten by a baseline there. Usable with margin and monitoring; validate on your own configuration. All verdicts →
8-layer [+45/-45]_2s angle-ply laminate with rotated thermal conductivity tensor, Kamal-Sourour kinetics, 40 spatial nodes.
Canonical benchmark implementation
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.
def _kamal_sourour_rate_vec(T: np.ndarray, alpha: np.ndarray) -> np.ndarray:
"""Vectorised Kamal-Sourour autocatalytic cure rate.
Clamps inputs for numerical safety before evaluating the Arrhenius
terms. Returns dα/dt for each node.
"""
T_safe = np.clip(T, _T_FLOOR, _T_CEIL)
alpha_safe = np.clip(alpha, 0.0, 1.0)
inv_RT = 1.0 / (_R_GAS * T_safe)
arg1 = np.clip(_E1 * inv_RT, 0.0, _EXP_ARG_MAX)
arg2 = np.clip(_E2 * inv_RT, 0.0, _EXP_ARG_MAX)
k1 = _A1 * np.exp(-arg1)
k2 = _A2 * np.exp(-arg2)
return (k1 + k2 * np.power(alpha_safe, _M)) * np.power(1.0 - alpha_safe, _N_ORD)
def _fp_angleply_8layer_rhs(t, y):
T = np.clip(y[:_AP_N_SPATIAL], _T_FLOOR, _T_CEIL)
alpha = np.clip(y[_AP_N_SPATIAL:2 * _AP_N_SPATIAL], 0.0, 1.0)
P = y[2 * _AP_N_SPATIAL:]
dadt = _kamal_sourour_rate_vec(T, alpha)
dT = np.empty(_AP_N_SPATIAL)
dP = np.empty(_AP_N_SPATIAL)
for layer in range(_AP_LAYERS):
start = layer * _AP_NODES_PER_LAYER
end = start + _AP_NODES_PER_LAYER
for i in range(start, end):
local = i - start
if i == 0:
T_left = _AP_T_BOTTOM
elif local == 0:
# Inter-ply interface (uniform k on both sides for ±45°)
R_half = _AP_DX / (2.0 * _AP_K_EFF)
R_left = R_half + _R_CONTACT
R_right = R_half
T_left = (T[i - 1] / R_left + T[i] / R_right) / (1.0 / R_left + 1.0 / R_right)
else:
T_left = T[i - 1]
if i == _AP_N_SPATIAL - 1:
# Top node: convective BC
T_right = T[i] + (_H_CONV * _AP_DX / _AP_K_EFF) * (_T_AMBIENT - T[i])
elif local == _AP_NODES_PER_LAYER - 1 and layer < _AP_LAYERS - 1:
R_half = _AP_DX / (2.0 * _AP_K_EFF)
R_left = R_half
R_right = R_half + _R_CONTACT
T_right = (T[i] / R_left + T[i + 1] / R_right) / (1.0 / R_left + 1.0 / R_right)
else:
T_right = T[i + 1]
lap = (T_left - 2.0 * T[i] + T_right) * _AP_INV_DX2
dT[i] = _AP_DIFF * lap + _SRC_COEFF * dadt[i]
dT[0] = 0.0
dP[:] = (
(_RHO_RESIN * _V_GAS_SPECIFIC * dadt * _R_GAS_IDEAL * T) / _V_PORE
- P * _PERM_LOSS
)
dy = np.empty(_AP_DIM)
dy[:_AP_N_SPATIAL] = dT
dy[_AP_N_SPATIAL:2 * _AP_N_SPATIAL] = dadt
dy[2 * _AP_N_SPATIAL:] = dP
return dyCanonical 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: extreme
Default noise: high
Recommendation snapshot
Clean best: SciPy DOP853
Noisy best: SolvSRK
Coverage
14 solver arms · clean + 5 noise levels
Ranked on survival, precision, and speed
20 seeds/cell default · 14 arms · TRL 4–5 · simulation-lab validated · this page: Frontal polymerization Angleply 8layer (frontal-polymerization-angleply-8layer)
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
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
| # | Solver | Survival | SCD | nfev | Wall | Score |
|---|---|---|---|---|---|---|
| 1 | SciPy DOP853SciPy | 100% | 11.8 | 52,502 | 3.58 s | 0.901 |
| 2 | Tsit5external | 100% | 11.1 | 60,774 | 10.10 s | 0.884 |
| 3 | SolvSRK | 100% | 11.1 | 59,047 | 4.30 s | 0.883 |
| 4 | SciPy RK45SciPy | 100% | 11.1 | 59,048 | 4.10 s | 0.882 |
| 5 | SciPy RadauSciPy | 100% | 10.3 | 4,744 | 410 ms | 0.865 |
| 6 | SciPy RK23SciPy | 100% | 10.0 | 33,827 | 2.43 s | 0.856 |
| 7 | SciPy LSODASciPy | 100% | 7.9 | 4,267 | 283 ms | 0.807 |
| 8 | CVODE BDFexternal | 100% | 7.5 | 1,451 | 110 ms | 0.798 |
| 9 | SciPy BDFSciPy | 100% | 7.4 | 1,657 | 157 ms | 0.795 |
| 10 | CVODE Adamsexternal | 100% | 7.1 | 11,425 | 836 ms | 0.787 |
At Clean, best balanced arm is SciPy DOP853 · SolvSRK survival 100%, SCD 11.1.
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 →
SolvScout · free
This page shows one published benchmark cell. SolvScout fingerprints your ODE, compares it to the full corpus, and recommends a solver with the same survival / precision / speed ranking you see here — including when a SciPy arm wins.
SolvSRK · 30-day trial
SolvSRK is the stiffness-adaptive integrator behind the SolvSRK column in these tables. Create an account, activate a machine, and take a 30-day trial — same binary you'd ship after purchase.
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_frontal_polymerization_angleply_8layer_2026,
title = {Resonix Evidence Portal: Frontal polymerization Angleply 8layer},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonix.tech/evidence/problems/frontal-polymerization-angleply-8layer}},
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.}
}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