Ablation Charring Pyrolysis
ADVANTAGES3 · dim 18SolvSRK 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 →
5-layer carbon phenolic TPS with Arrhenius pyrolysis, Darcy gas transport through porous char, and thermal response. Stiffness from fast chemical decomposition vs slow thermal diffusion.
Problem definition
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.
Show canonical RHS excerpt
def _arrhenius_rate(T, alpha):
"""Arrhenius decomposition rate with numerical safeguards."""
T_safe = np.clip(T, 200.0, 5000.0)
remaining = np.clip(1.0 - alpha, 0.0, 1.0)
exp_term = np.exp(-_EA_DECOMP / (_R_GAS * T_safe))
return _A_DECOMP * exp_term * remaining ** _N_DECOMP
def _permeability(alpha):
"""Darcy permeability increasing with char fraction."""
return 1.0e-12 * (1.0 + 10.0 * np.clip(alpha, 0.0, 1.0))
def _thermal_conductivity(alpha):
"""Effective conductivity: 0.5 (virgin) → 2.0 (char) W/(m·K)."""
return 0.5 + 1.5 * np.clip(alpha, 0.0, 1.0)
def _charring_pyrolysis_rhs(t, y):
dy = np.zeros(18)
alpha = np.clip(y[0:5], 0.0, 1.0)
T = np.clip(y[5:10], 200.0, 5000.0)
gas_flux = y[10:13]
P = np.maximum(y[13:18], 1000.0)
# --- Decomposition (Arrhenius) ---
rate = _arrhenius_rate(T, alpha)
dy[0:5] = rate
# --- Gas generation from pyrolysis ---
gas_gen = np.zeros(_NL1)
for i in range(_NL1):
gas_gen[i] = _RHO_VIRGIN * rate[i] * _V_LAYER
# --- Gas pressure (ideal-gas-like response to generation + flow) ---
K_perm = _permeability(alpha)
dx_inv = 1.0 / _DX1
for i in range(_NL1):
dP_dx_in = 0.0
dP_dx_out = 0.0
if i > 0:
dP_dx_in = (P[i - 1] - P[i]) * dx_inv
if i < _NL1 - 1:
dP_dx_out = (P[i] - P[i + 1]) * dx_inv
flux_in = (K_perm[i] / _MU_GAS) * dP_dx_in if i > 0 else 0.0
flux_out = (K_perm[i] / _MU_GAS) * dP_dx_out if i < _NL1 - 1 else 0.0
# Layer 0 (surface) vents to atmosphere
if i == 0:
flux_out_vent = (K_perm[0] / _MU_GAS) * (P[0] - _P_ATM) * dx_inv
net_flux = flux_in - flux_out_vent
else:
net_flux = flux_in - flux_out
# P evolves from generation and net flux divergence
# dp/dt ~ (gas_gen * R_specific * T / V - net_flux_divergence * P) / (rho * V)
dp = gas_gen[i] * _R_GAS * T[i] / (0.029 * _V_LAYER) + net_flux * dx_inv * 1e3
dy[13 + i] = dp
# --- Gas mass flux at 3 interior interfaces (between layers 0-1, 1-2, 2-3) ---
for j in range(3):
i = j # interface between layer j and j+1
K_avg = 0.5 * (K_perm[i] + K_perm[i + 1])
dP = P[i] - P[i + 1]
flux = -(K_avg / _MU_GAS) * dP * dx_inv
tau_flux = 0.01 # relaxation timescale for flux
dy[10 + j] = (flux - gas_flux[j]) / tau_flux
# --- Energy equation ---
k_eff = _thermal_conductivity(alpha)
rho_cp = _RHO_VIRGIN * _CP # simplified (should interpolate virgin→char)
for i in range(_NL1):
# Conduction (finite differences)
if i == 0:
# Surface: radiative + convective heating from environment
q_rad = _EPSILON * _SIGMA_SB * (_T_RAD**4 - T[0]**4)
q_conv = _H_CONV * (_T_RAD - T[0])
q_in = q_rad + q_conv
q_cond = k_eff[0] * (T[1] - T[0]) / (_DX1 * _DX1)
dT = (q_in / _DX1 + q_cond) / rho_cp
elif i == _NL1 - 1:
# Insulated back face
q_cond = k_eff[i] * (T[i - 1] - T[i]) / (_DX1 * _DX1)
dT = q_cond / rho_cp
else:
# Interior: central difference
q_cond = k_eff[i] * (T[i - 1] - 2.0 * T[i] + T[i + 1]) / (_DX1 * _DX1)
dT = q_cond / rho_cp
# Pyrolysis heat source (endothermic)
dT += _Q_PYROLYSIS * rate[i] / _CP
# Gas convective cooling within pores
if i < 3:
gas_cooling = _H_GAS * gas_flux[min(i, 2)] * (T[i] - _T_GAS_IN)
dT -= gas_cooling / rho_cp
dy[5 + i] = dT
return dy- Parameters
- _A_DECOMP = 1e+10
- _CP = 1200
- _DX1 = 0.004
- _EA_DECOMP = 120000
- _EPSILON = 0.85
- _H_CONV = 200
- _H_GAS = 50
- _MU_GAS = 3e-05
- _NL1 = 5
- _N_DECOMP = 1.5
- _P_ATM = 101325
- _Q_PYROLYSIS = -250000
- _RHO_VIRGIN = 1400
- _R_GAS = 8.314
- _SIGMA_SB = 5.67037e-08
- _T_GAS_IN = 500
- _T_RAD = 2500
- _V_LAYER = 0.004
- Initial condition
- y(0) = [0, 0, 0, 0, 0, 300, …] [shape=(18,), min=0, max=101325]
- Horizon
- t ∈ [0, 600]
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: extreme
Default noise: high
Recommendation snapshot
Clean best: SolvSRK
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: Ablation Charring Pyrolysis (ablation-charring-pyrolysis)
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
| # | Solver | Survival | SCD | nfev | Wall | Score |
|---|---|---|---|---|---|---|
| 1 | SolvSRK | 100% | 14.1 | 11,401 | 505 ms | 0.955 |
| 2 | SciPy RadauSciPy | 100% | 12.3 | 25,152 | 1.40 s | 0.912 |
| 3 | SciPy DOP853SciPy | 100% | 11.5 | 137,438 | 6.13 s | 0.892 |
| 4 | Tsit5external | 100% | 11.3 | 135,540 | 18.73 s | 0.889 |
| 5 | SciPy RK45SciPy | 100% | 11.2 | 147,482 | 6.71 s | 0.885 |
| 6 | SciPy RK23SciPy | 100% | 11.0 | 96,683 | 4.62 s | 0.881 |
| 7 | SciPy LSODASciPy | 100% | 9.4 | 9,586 | 406 ms | 0.843 |
| 8 | CVODE BDFexternal | 100% | 9.2 | 3,243 | 158 ms | 0.839 |
| 9 | SciPy BDFSciPy | 100% | 8.9 | 6,961 | 472 ms | 0.831 |
| 10 | CVODE Adamsexternal | 100% | 8.5 | 9,116 | 416 ms | 0.821 |
At Clean, best balanced arm is SolvSRK.
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 →
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_ablation_charring_pyrolysis_2026,
title = {Resonix Evidence Portal: Ablation Charring Pyrolysis},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonixusa.com/evidence/problems/ablation-charring-pyrolysis}},
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