Overview Evidence tour Methodology Dead zones Problems

FP Glass Fiber 3D

PARITYS3 · dim 686

No clear winner. The survival gap is under 10 percentage points and the balanced-score gap is under 0.05, so neither SolvSRK nor the best baseline clears the win threshold. Either works - choose on cost, licensing, or integration effort. All verdicts →

7x7x7 3D glass-fiber front propagation (dim=686, S3)

Materials & composites

Problem definition

Kamal & Sourour (1973); Pojman (2012)

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 _idx3d(i, j, k):
    return i * _GF3_NY * _GF3_NZ + j * _GF3_NZ + k

def _kamal_sourour_rate_vec(T: np.ndarray, alpha: np.ndarray) -> np.ndarray:
    T_safe = np.clip(T, _T_FLOOR, _T_CEIL)
    alpha_safe = np.clip(alpha, 0.0, 1.0)
    inv_RT = 1.0 / (_R_GAS * T_safe)
    arg1 = np.clip(_E1 * inv_RT, 0.0, _EXP_ARG_MAX)
    arg2 = np.clip(_E2 * inv_RT, 0.0, _EXP_ARG_MAX)
    k1 = _A1 * np.exp(-arg1)
    k2 = _A2 * np.exp(-arg2)
    return (k1 + k2 * np.power(alpha_safe, _M)) * np.power(1.0 - alpha_safe, _N_ORD)

def _fp_glass_fiber_3d_rhs(t, y):
    T_flat = np.clip(y[:_GF3_N3D], _T_FLOOR, _T_CEIL)
    alpha_flat = np.clip(y[_GF3_N3D:], 0.0, 1.0)

    dadt = _kamal_sourour_rate_vec(T_flat, alpha_flat)
    dT = np.empty(_GF3_N3D)

    for i in range(_GF3_NX):
        for j in range(_GF3_NY):
            for k in range(_GF3_NZ):
                idx = _idx3d(i, j, k)
                T_c = T_flat[idx]

                # x-direction (fiber): Dirichlet at i=0, convective at i=NX-1
                if i == 0:
                    lap_x = 0.0
                elif i == _GF3_NX - 1:
                    T_ghost = T_c + (_H_CONV * _GF3_DX / _K_FIBER_G) * (_T_AMBIENT - T_c)
                    lap_x = (T_flat[_idx3d(i - 1, j, k)] - 2.0 * T_c + T_ghost) * _GF3_INV_DX2
                else:
                    T_left = _GF3_T_LEFT if i == 1 else T_flat[_idx3d(i - 1, j, k)]
                    lap_x = (T_left - 2.0 * T_c + T_flat[_idx3d(i + 1, j, k)]) * _GF3_INV_DX2

                # y-direction (transverse)
                if j == 0:
                    T_ghost = T_c + (_H_CONV * _GF3_DY / _K_TRANS_G) * (_T_AMBIENT - T_c)
                    lap_y = (T_ghost - 2.0 * T_c + T_flat[_idx3d(i, j + 1, k)]) * _GF3_INV_DY2
                elif j == _GF3_NY - 1:
                    T_ghost = T_c + (_H_CONV * _GF3_DY / _K_TRANS_G) * (_T_AMBIENT - T_c)
                    lap_y = (T_flat[_idx3d(i, j - 1, k)] - 2.0 * T_c + T_ghost) * _GF3_INV_DY2
                else:
                    lap_y = (T_flat[_idx3d(i, j - 1, k)] - 2.0 * T_c + T_flat[_idx3d(i, j + 1, k)]) * _GF3_INV_DY2

                # z-direction (transverse)
                if k == 0:
                    T_ghost = T_c + (_H_CONV * _GF3_DZ / _K_TRANS_G) * (_T_AMBIENT - T_c)
                    lap_z = (T_ghost - 2.0 * T_c + T_flat[_idx3d(i, j, k + 1)]) * _GF3_INV_DZ2
                elif k == _GF3_NZ - 1:
                    T_ghost = T_c + (_H_CONV * _GF3_DZ / _K_TRANS_G) * (_T_AMBIENT - T_c)
                    lap_z = (T_flat[_idx3d(i, j, k - 1)] - 2.0 * T_c + T_ghost) * _GF3_INV_DZ2
                else:
                    lap_z = (T_flat[_idx3d(i, j, k - 1)] - 2.0 * T_c + T_flat[_idx3d(i, j, k + 1)]) * _GF3_INV_DZ2

                dT[idx] = (_GF3_DIFF_X * lap_x + _GF3_DIFF_Y * lap_y
                           + _GF3_DIFF_Z * lap_z + _SRC_COEFF * dadt[idx])

    # Left face (i=0) is Dirichlet
    for j in range(_GF3_NY):
        for k in range(_GF3_NZ):
            dT[_idx3d(0, j, k)] = 0.0

    dy = np.empty(_GF3_DIM)
    dy[:_GF3_N3D] = dT
    dy[_GF3_N3D:] = dadt
    return dy

Parameters: _A1 = 20000
_A2 = 1.5e+06
_E1 = 60000
_E2 = 75000
_EXP_ARG_MAX = 500
_GF3_DIFF_X = 6.21118e-07
_GF3_DIFF_Y = 1.24224e-07
_GF3_DIFF_Z = 1.24224e-07
_GF3_DIM = 686
_GF3_DX = 0.0012
_GF3_DY = 0.0012
_GF3_DZ = 0.0012
_GF3_INV_DX2 = 694444
_GF3_INV_DY2 = 694444
_GF3_INV_DZ2 = 694444
_GF3_N3D = 343
_GF3_NX = 7
_GF3_NY = 7
_GF3_NZ = 7
_GF3_T_LEFT = 523.15
_H_CONV = 10
_K_FIBER_G = 1
_K_TRANS_G = 0.2
_M = 0.8
_N_ORD = 1.8
_R_GAS = 8.314
_SRC_COEFF = 250
_T_AMBIENT = 298
_T_CEIL = 5000
_T_FLOOR = 200
Initial condition: y(0) = [523.15, 523.15, 523.15, 523.15, 523.15, 523.15, …] [shape=(686,), min=0.001, max=523.15]
Horizon: t ∈ [0, 360]

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: low

Recommendation snapshot

Clean best: SolvSRK

Noisy best: SciPy BDF

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: FP Glass Fiber 3D (fp-glass-fiber-3d)

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%	12.0	2,449	1.93 s	0.906
2	SciPy RadauSciPy	100%	10.3	26,993	24.11 s	0.866
3	SciPy LSODASciPy	100%	8.4	4,365	3.59 s	0.819
4	CVODE Adamsexternal	100%	8.3	8,281	7.62 s	0.817
5	SciPy DOP853SciPy	100%	8.1	1,922	1.55 s	0.811
6	SciPy RK45SciPy	100%	7.9	2,018	1.63 s	0.808
7	Tsit5external	100%	7.9	2,064	2.78 s	0.808
8	CVODE BDFexternal	100%	7.9	6,699	5.90 s	0.806
9	SciPy BDFSciPy	100%	7.7	2,526	2.72 s	0.802
10	SciPy RK23SciPy	100%	7.3	3,335	2.71 s	0.793

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_fp_glass_fiber_3d_2026,
  title        = {Resonix Evidence Portal: FP Glass Fiber 3D},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonixusa.com/evidence/problems/fp-glass-fiber-3d}},
  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.}
}

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

Problem definition

Versions & freeze

Results matrix

Cite this page

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