Ablation Surface Multispecies

ADVANTAGES3 · dim 22

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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.

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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 _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

#SolverSurvivalSCDnfevWallScore
1SolvSRK
100%
11.64,259172 ms0.894
2SciPy RadauSciPy
100%
10.86,552466 ms0.877
3SciPy RK23SciPy
100%
9.120,8611.26 s0.836
4SciPy DOP853SciPy
100%
8.929,2701.68 s0.832
5SciPy RK45SciPy
100%
8.331,9941.87 s0.818
6Tsit5external
100%
8.232,9765.85 s0.815
7SciPy LSODASciPy
100%
8.02,289126 ms0.810
8CVODE BDFexternal
100%
7.41,32982 ms0.796
9SciPy BDFSciPy
100%
7.32,292193 ms0.794
10CVODE Adamsexternal
100%
7.03,093186 ms0.787

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 →

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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_surface_multispecies_2026,
  title        = {Resonix Evidence Portal: Ablation Surface Multispecies},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/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