Ablation Charring Pyrolysis

ADVANTAGES3 · dim 18

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 →

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.

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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

#SolverSurvivalSCDnfevWallScore
1SolvSRK
100%
14.111,401505 ms0.955
2SciPy RadauSciPy
100%
12.325,1521.40 s0.912
3SciPy DOP853SciPy
100%
11.5137,4386.13 s0.892
4Tsit5external
100%
11.3135,54018.73 s0.889
5SciPy RK45SciPy
100%
11.2147,4826.71 s0.885
6SciPy RK23SciPy
100%
11.096,6834.62 s0.881
7SciPy LSODASciPy
100%
9.49,586406 ms0.843
8CVODE BDFexternal
100%
9.23,243158 ms0.839
9SciPy BDFSciPy
100%
8.96,961472 ms0.831
10CVODE Adamsexternal
100%
8.59,116416 ms0.821

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 →

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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_ablation_charring_pyrolysis_2026,
  title        = {Resonix Evidence Portal: Ablation Charring Pyrolysis},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/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