Ablation Recession Coupled
ADVANTAGES3 · dim 15SolvSRK 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 →
Full TPS recession model with moving-boundary formulation, pyrolysis gas permeation, and Arrhenius decomposition on 6-node grid. Moving boundary adds algebraic coupling to the stiff kinetics.
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 _recession_coupled_rhs(t, y):
dy = np.zeros(15)
T = np.clip(y[0:6], 200.0, 5000.0)
alpha = np.clip(y[6:9], 0.0, 1.0)
gas_flux = y[9:12]
s = max(y[12], 0.0) # recession (non-negative)
T_surf = np.clip(y[13], 200.0, 5000.0)
char_thick = max(y[14], 0.0)
L_eff = max(_L_TPS - s, 1.0e-4) # remaining TPS thickness, bounded away from zero
L_inv = 1.0 / L_eff
L_inv2 = L_inv * L_inv
# --- Surface recession rate (B' formulation) ---
B_prime = 0.5 * np.exp(-_EA_ABLATION / (_R_GAS * T_surf))
m_dot_abl = B_prime * _RHO_E * _U_E * _C_H
ds_dt = m_dot_abl / _RHO_CHAR
# --- Thermal conductivity and diffusivity ---
# Char fraction at each node: nodes 0,1 assumed fully charred near surface;
# nodes 2,3,4 use the interior decomposition state; node 5 is virgin.
alpha_full = np.zeros(_NT3)
alpha_full[0] = 1.0
alpha_full[1] = 1.0
alpha_full[2:5] = alpha
alpha_full[5] = 0.0
k_eff = _thermal_conductivity(alpha_full)
rho_cp = _RHO_VIRGIN * _CP
kappa = k_eff / rho_cp # thermal diffusivity per node
# --- Energy equation in moving frame ---
# dT/dt = κ * d²T/dξ² / (L-s)² + (ds/dt)*ξ/(L-s)*dT/dξ + Q_pyro/(ρ*cp)*dα/dt
dxi = _DXI
for i in range(_NT3):
xi = i * dxi # transformed coordinate
# d²T/dξ² via finite differences
if i == 0:
# Surface node: radiative + convective + ablation enthalpy
q_rad = _EPSILON * _SIGMA_SB * (_T_RAD**4 - T[0]**4)
q_conv = _H_CONV * (_T_RAD - T[0])
q_abl = -m_dot_abl * 3.0e6 # ablation enthalpy (J/kg)
# One-sided second derivative
d2T = (T[1] - 2.0 * T[0] + T[0]) / (dxi * dxi) # ghost = T[0] (Neumann-like)
dT_dxi = (T[1] - T[0]) / dxi
dT = kappa[0] * d2T * L_inv2 + ds_dt * xi * L_inv * dT_dxi
dT += (q_rad + q_conv + q_abl) / (_DXI * L_eff * rho_cp)
elif i == _NT3 - 1:
# Back face: insulated (dT/dξ = 0 at ξ=1)
d2T = (T[i - 1] - T[i]) / (dxi * dxi) # ghost T[N] = T[N-1]
dT_dxi = 0.0
dT = kappa[i] * d2T * L_inv2
else:
d2T = (T[i - 1] - 2.0 * T[i] + T[i + 1]) / (dxi * dxi)
dT_dxi = (T[i + 1] - T[i - 1]) / (2.0 * dxi)
dT = kappa[i] * d2T * L_inv2 + ds_dt * xi * L_inv * dT_dxi
# Pyrolysis source at interior nodes
if 2 <= i <= 4:
j = i - 2
pyro_rate = _arrhenius_rate(T[i], alpha[j])
dT += _Q_PYROLYSIS * pyro_rate / _CP
dy[i] = dT
# --- Interior decomposition (nodes 2, 3, 4 → indices 0, 1, 2 in alpha) ---
for j in range(3):
T_node = T[j + 2]
dy[6 + j] = _arrhenius_rate(T_node, alpha[j])
# --- Gas mass flux at 3 interior points ---
K_perm_avg = _permeability(alpha)
P_grad_scale = 5000.0 # Pa/m characteristic pressure gradient from pyrolysis
for j in range(3):
target_flux = -(K_perm_avg[j] / _MU_GAS) * P_grad_scale * dy[6 + j]
tau_f = 0.05
dy[9 + j] = (target_flux - gas_flux[j]) / tau_f
# --- Recession and char thickness ---
dy[12] = ds_dt
# Surface temperature tracks grid node 0 with slight lag (separate ODE for stiffness)
tau_surf = 0.1
dy[13] = (T[0] - T_surf) / tau_surf
# Char thickness grows as decomposition front advances
mean_alpha = np.mean(alpha)
dy[14] = ds_dt * 0.5 + _arrhenius_rate(T[2], mean_alpha) * _DXI * L_eff * 0.1
return dy- Parameters
- _A_DECOMP = 1e+10
- _CP = 1200
- _C_H = 0.002
- _DXI = 0.2
- _EA_ABLATION = 100000
- _EA_DECOMP = 120000
- _EPSILON = 0.85
- _H_CONV = 200
- _L_TPS = 0.025
- _MU_GAS = 3e-05
- _NT3 = 6
- _N_DECOMP = 1.5
- _Q_PYROLYSIS = -250000
- _RHO_CHAR = 400
- _RHO_E = 0.05
- _RHO_VIRGIN = 1400
- _R_GAS = 8.314
- _SIGMA_SB = 5.67037e-08
- _T_RAD = 2500
- _U_E = 3000
- Initial condition
- y(0) = [300, 300, 300, 300, 300, 300, …] [shape=(15,), min=0, max=300]
- Horizon
- t ∈ [0, 900]
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: Tsit5
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 Recession Coupled (ablation-recession-coupled)
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 | Tsit5external | 100% | 11.9 | 38,550 | 7.13 s | 0.902 |
| 2 | SolvSRK | 100% | 11.3 | 36,379 | 2.79 s | 0.888 |
| 3 | SciPy DOP853SciPy | 100% | 11.1 | 34,154 | 2.53 s | 0.882 |
| 4 | SciPy RadauSciPy | 100% | 11.1 | 5,624 | 492 ms | 0.882 |
| 5 | SciPy RK45SciPy | 100% | 11.0 | 36,374 | 2.69 s | 0.881 |
| 6 | SciPy RK23SciPy | 100% | 8.8 | 22,361 | 1.72 s | 0.828 |
| 7 | CVODE BDFexternal | 100% | 7.4 | 1,038 | 90 ms | 0.794 |
| 8 | SciPy LSODASciPy | 100% | 7.4 | 2,054 | 148 ms | 0.794 |
| 9 | CVODE Adamsexternal | 100% | 7.0 | 3,402 | 260 ms | 0.786 |
| 10 | SciPy BDFSciPy | 100% | 7.0 | 2,281 | 228 ms | 0.785 |
At Clean, best balanced arm is Tsit5 · SolvSRK survival 100%, SCD 11.3.
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 →
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_recession_coupled_2026,
title = {Resonix Evidence Portal: Ablation Recession Coupled},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonixusa.com/evidence/problems/ablation-recession-coupled}},
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