Ablation Surface Multispecies
ADVANTAGES3 · dim 22SolvSRK 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 →
Surface ablation with 6 gas species (CO, CO2, H2, H2O, N2, O2), heterogeneous surface reactions, and 8-node thermal response. Extreme stiffness from competing Arrhenius surface chemistry.
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 _multispecies_rhs(t, y):
dy = np.zeros(22)
Y = np.clip(y[0:6], 0.0, 1.0)
T = np.clip(y[6:14], 200.0, 5000.0)
recession = y[14]
rec_rate = y[15]
char_thick = max(y[16], 0.0)
alpha_sub = np.clip(y[17:22], 0.0, 1.0)
T_surf = T[0]
# --- Heterogeneous surface reactions ---
r1 = _K1 * Y[_I_O2] * np.exp(-_E1 / (_R_GAS * T_surf))
r2 = _K2 * Y[_I_CO2] * np.exp(-_E2 / (_R_GAS * T_surf))
r3 = _K3 * Y[_I_H2O] * np.exp(-_E3 / (_R_GAS * T_surf))
total_ablation_rate = r1 + r2 + r3
dm_dt = total_ablation_rate # mass loss rate per unit area
# Molar masses: CO=28, CO₂=44, H₂=2, H₂O=18, N₂=28, O₂=32
# Species production/consumption per unit area:
# R1: -O₂ (32g), +CO₂ (44g)
# R2: -CO₂ (44g), +2CO (56g)
# R3: -H₂O (18g), +CO (28g), +H₂ (2g)
prod = np.zeros(6)
prod[_I_CO2] = r1 * (44.0 / 32.0) - r2
prod[_I_CO] = 2.0 * r2 * (28.0 / 44.0) + r3 * (28.0 / 18.0)
prod[_I_H2] = r3 * (2.0 / 18.0)
prod[_I_H2O] = -r3
prod[_I_O2] = -r1
prod[_I_N2] = 0.0
# Species mass fraction evolution
for i in range(6):
dy[i] = (prod[i] - Y[i] * dm_dt) / _M_SURFACE
# Freestream entrainment drives species back toward freestream composition
_tau_mix = 0.5 # s, mixing timescale
Y_free = np.array([0.0, 0.0, 0.0, 0.0, 0.77, 0.23])
dy[0:6] += (Y_free - Y) / _tau_mix
# --- Thermal (8-node 1D conduction) ---
k_nodes = np.full(_N_THERMAL, 1.5) # W/(m·K) char conductivity
rho_cp = _RHO_CHAR2 * _CP
dx2_inv = 1.0 / (_DX2 * _DX2)
# Surface node: radiative + convective heating + reaction enthalpy
q_rad = _EPSILON * _SIGMA_SB * (_T_RAD**4 - T[0]**4)
q_conv = _H_CONV * (_T_RAD - T[0])
q_react = -total_ablation_rate * 1.5e6 # net exothermic surface reactions (J/kg * rate)
q_cond_0 = k_nodes[0] * (T[1] - T[0]) * dx2_inv
dy[6] = (q_rad + q_conv + q_react) / (_DX2 * rho_cp) + q_cond_0 / rho_cp
# Interior nodes
for i in range(1, _N_THERMAL - 1):
k_avg_l = 0.5 * (k_nodes[i - 1] + k_nodes[i])
k_avg_r = 0.5 * (k_nodes[i] + k_nodes[i + 1])
q_cond = (k_avg_l * (T[i - 1] - T[i]) + k_avg_r * (T[i + 1] - T[i])) * dx2_inv
dy[6 + i] = q_cond / rho_cp
# Back face: insulated
k_avg = 0.5 * (k_nodes[_N_THERMAL - 2] + k_nodes[_N_THERMAL - 1])
dy[6 + _N_THERMAL - 1] = k_avg * (T[_N_THERMAL - 2] - T[_N_THERMAL - 1]) * dx2_inv / rho_cp
# --- Surface recession ---
ds_dt = total_ablation_rate / _RHO_CHAR2
dy[14] = ds_dt # total recession
tau_rec = 1.0 # smoothing timescale
dy[15] = (ds_dt - rec_rate) / tau_rec # recession rate (smoothed)
dy[16] = max(ds_dt * 0.3, 0.0) # char thickness grows (fraction of recession)
# --- Sublayer decomposition ---
for i in range(5):
T_sub = T[min(i + 1, _N_THERMAL - 1)]
dy[17 + i] = _arrhenius_rate(T_sub, alpha_sub[i])
return dy- Parameters
- _A_DECOMP = 1e+10
- _CP = 1200
- _DX2 = 0.003
- _E1 = 150000
- _E2 = 200000
- _E3 = 180000
- _EA_DECOMP = 120000
- _EPSILON = 0.85
- _H_CONV = 200
- _I_CO = 0
- _I_CO2 = 1
- _I_H2 = 2
- _I_H2O = 3
- _I_N2 = 4
- _I_O2 = 5
- _K1 = 50000
- _K2 = 2000
- _K3 = 1000
- _M_SURFACE = 0.5
- _N_DECOMP = 1.5
- _N_THERMAL = 8
- _RHO_CHAR2 = 500
- _R_GAS = 8.314
- _SIGMA_SB = 5.67037e-08
- _T_RAD = 2500
- Initial condition
- y(0) = [0, 0, 0, 0, 0.77, 0.23, …] [shape=(22,), min=0, max=300]
- Horizon
- t ∈ [0, 300]
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: none
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 Surface Multispecies (ablation-surface-multispecies)
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% | 11.6 | 4,259 | 172 ms | 0.894 |
| 2 | SciPy RadauSciPy | 100% | 10.8 | 6,552 | 466 ms | 0.877 |
| 3 | SciPy RK23SciPy | 100% | 9.1 | 20,861 | 1.26 s | 0.836 |
| 4 | SciPy DOP853SciPy | 100% | 8.9 | 29,270 | 1.68 s | 0.832 |
| 5 | SciPy RK45SciPy | 100% | 8.3 | 31,994 | 1.87 s | 0.818 |
| 6 | Tsit5external | 100% | 8.2 | 32,976 | 5.85 s | 0.815 |
| 7 | SciPy LSODASciPy | 100% | 8.0 | 2,289 | 126 ms | 0.810 |
| 8 | CVODE BDFexternal | 100% | 7.4 | 1,329 | 82 ms | 0.796 |
| 9 | SciPy BDFSciPy | 100% | 7.3 | 2,292 | 193 ms | 0.794 |
| 10 | CVODE Adamsexternal | 100% | 7.0 | 3,093 | 186 ms | 0.787 |
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_surface_multispecies_2026,
title = {Resonix Evidence Portal: Ablation Surface Multispecies},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonixusa.com/evidence/problems/ablation-surface-multispecies}},
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