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FP Woven Fabric 2D High-Res

PARITYS3 · dim 675

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 →

15x15 woven fabric composite with checkerboard conductivity and gas pressure (dim=675, 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 _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_woven_fabric_2d_rhs(t, y):
    T_flat = np.clip(y[:_WF_N2D], _T_FLOOR, _T_CEIL)
    alpha_flat = np.clip(y[_WF_N2D:2 * _WF_N2D], 0.0, 1.0)
    P_flat = y[2 * _WF_N2D:]

    T = T_flat.reshape(_WF_NX, _WF_NY)
    alpha = alpha_flat.reshape(_WF_NX, _WF_NY)

    dadt_2d = _kamal_sourour_rate_vec(T, alpha)
    dT = np.empty((_WF_NX, _WF_NY))

    for i in range(_WF_NX):
        for j in range(_WF_NY):
            kx = _WF_KX[i, j]
            diff_x = _WF_DIFF_X[i, j]
            diff_y = _WF_DIFF_Y[i, j]
            ky = _WF_KY[i, j]

            # x-direction Laplacian
            if j == 0:
                lap_x = 0.0  # Left edge Dirichlet
            elif j == _WF_NY - 1:
                T_ghost = T[i, j] + (_H_CONV * _WF_DX / kx) * (_T_AMBIENT - T[i, j])
                lap_x = (T[i, j - 1] - 2.0 * T[i, j] + T_ghost) * _WF_INV_DX2
            else:
                T_left = _WF_T_LEFT if j == 1 else T[i, j - 1]
                lap_x = (T_left - 2.0 * T[i, j] + T[i, j + 1]) * _WF_INV_DX2

            # y-direction Laplacian
            if i == 0:
                T_ghost = T[i, j] + (_H_CONV * _WF_DY / ky) * (_T_AMBIENT - T[i, j])
                lap_y = (T_ghost - 2.0 * T[i, j] + T[i + 1, j]) * _WF_INV_DY2
            elif i == _WF_NX - 1:
                T_ghost = T[i, j] + (_H_CONV * _WF_DY / ky) * (_T_AMBIENT - T[i, j])
                lap_y = (T[i - 1, j] - 2.0 * T[i, j] + T_ghost) * _WF_INV_DY2
            else:
                lap_y = (T[i - 1, j] - 2.0 * T[i, j] + T[i + 1, j]) * _WF_INV_DY2

            dT[i, j] = diff_x * lap_x + diff_y * lap_y + _SRC_COEFF * dadt_2d[i, j]

    dT[:, 0] = 0.0

    # Gas pressure
    dP = (
        (_RHO_RESIN * _V_GAS_SPECIFIC * dadt_2d.ravel() * _R_GAS_IDEAL * T_flat) / _V_PORE
        - P_flat * _PERM_LOSS
    )

    dy = np.empty(_WF_DIM)
    dy[:_WF_N2D] = dT.ravel()
    dy[_WF_N2D:2 * _WF_N2D] = dadt_2d.ravel()
    dy[2 * _WF_N2D:] = dP
    return dy

Parameters: _A1 = 20000
_A2 = 1.5e+06
_E1 = 60000
_E2 = 75000
_EXP_ARG_MAX = 500
_H_CONV = 10
_M = 0.8
_N_ORD = 1.8
_PERM_LOSS = 0.001
_RHO_RESIN = 1150
_R_GAS = 8.314
_R_GAS_IDEAL = 8.314
_SRC_COEFF = 250
_T_AMBIENT = 298
_T_CEIL = 5000
_T_FLOOR = 200
_V_GAS_SPECIFIC = 0.02
_V_PORE = 0.01
_WF_DIFF_X = [3.10559e-06, 3.10559e-07, 3.10559e-06, 3.10559e-07, 3.10559e-06, 3.10559e-07, …] [shape=(15, 15), min=3.10559e-07, max=3.10559e-06]
_WF_DIFF_Y = [3.10559e-07, 3.10559e-06, 3.10559e-07, 3.10559e-06, 3.10559e-07, 3.10559e-06, …] [shape=(15, 15), min=3.10559e-07, max=3.10559e-06]
_WF_DIM = 675
_WF_DX = 0.0008
_WF_DY = 0.0008
_WF_INV_DX2 = 1.5625e+06
_WF_INV_DY2 = 1.5625e+06
_WF_KX = [5, 0.5, 5, 0.5, 5, 0.5, …] [shape=(15, 15), min=0.5, max=5]
_WF_KY = [0.5, 5, 0.5, 5, 0.5, 5, …] [shape=(15, 15), min=0.5, max=5]
_WF_N2D = 225
_WF_NX = 15
_WF_NY = 15
_WF_T_LEFT = 523.15
Initial condition: y(0) = [523.15, 298, 298, 298, 298, 298, …] [shape=(675,), min=0.001, max=101325]
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: high

Recommendation snapshot

Clean best: SciPy DOP853

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 Woven Fabric 2D High-Res (fp-woven-fabric-2d-high-res)

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	SciPy DOP853SciPy	100%	13.2	12,362	5.59 s	0.934
2	SolvSRK	100%	10.6	30,845	11.65 s	0.872
3	SciPy RK45SciPy	100%	10.4	13,106	5.99 s	0.866
4	SciPy RadauSciPy	100%	10.3	19,356	11.42 s	0.864
5	Tsit5external	100%	10.2	13,842	8.68 s	0.862
6	SciPy RK23SciPy	100%	8.6	9,521	4.35 s	0.823
7	SciPy LSODASciPy	100%	7.7	25,719	11.71 s	0.802
8	CVODE Adamsexternal	100%	7.5	17,251	9.19 s	0.797
9	CVODE BDFexternal	100%	7.3	7,361	3.68 s	0.793
10	SciPy BDFSciPy	100%	7.2	4,176	2.56 s	0.790

At Clean, best balanced arm is SciPy DOP853 · SolvSRK survival 100%, SCD 10.6.

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_woven_fabric_2d_high_res_2026,
  title        = {Resonix Evidence Portal: FP Woven Fabric 2D High-Res},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonixusa.com/evidence/problems/fp-woven-fabric-2d-high-res}},
  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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