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HEL Thermal Kill (10kW.AL.800)

ADVANTAGES2 · dim 8

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 →

HEL Thermal Kill (10kW.AL.800) benchmark in the defense-autonomy domain.

Defense autonomy

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 rhs(t, y):
    T_tgt = max(y[0], T_ambient)
    T_bat = max(y[1], T_ambient)
    T_pc  = max(y[2], T_ambient)
    sig_d = y[3]
    R     = max(y[5], 1.0)
    eps   = max(abs(y[7]), 1e-10)

    d = np.empty(8)

    # Atmospheric transmission (Beer-Lambert)
    tau_atm = np.exp(-beta_ext * R / 1000.0)

    # Tracking efficiency (Gaussian beam, fraction on target)
    spot_var = 2.0 * (eps * R) ** 2
    eta_track = 1.0 - np.exp(-A_target / max(spot_var, 1e-20))

    P_beam = P_laser * tau_atm * eta_track

    # Target surface heating
    Q_laser = alpha_abs * P_beam
    Q_conv = h_conv * A_t * (T_tgt - T_ambient)
    Q_rad = epsilon_t * _SIGMA_SB * A_t * (T_tgt**4 - T_ambient**4)
    d[0] = (Q_laser - Q_conv - Q_rad - k_cond_bt * (T_tgt - T_bat) - k_cond_pt * (T_tgt - T_pc)) / C_t

    # Battery heating (conduction from target + self-heating)
    d[1] = (k_cond_bt * (T_tgt - T_bat) + I_batt**2 * R_internal - h_b * (T_bat - T_ambient)) / C_b

    # PCB heating
    d[2] = (k_cond_pt * (T_tgt - T_pc) + P_electronics - h_pcb * (T_pc - T_ambient)) / C_pcb

    # Arrhenius damage accumulation
    d[3] = A_arrhenius * np.exp(-E_activation / (_K_B * max(T_tgt, 200.0)))

    # Beam power state (for diagnostics)
    d[4] = (P_beam - y[4]) / 0.01

    # Range decreasing
    d[5] = -V_close

    # Track angle rate (simplified LOS)
    d[6] = V_close * 0.01 / max(R, 1.0)

    # Tracking error settling
    d[7] = -(y[7] - sigma_jitter * 0.5) / tau_track

    return d

Parameters: A_arrhenius = 1e+08
A_t = 0.02
A_target = 0.05
C_b = 200
C_pcb = 80
C_t = 500
E_activation = 1.2e-19
I_batt = 20
P_electronics = 15
P_laser = 10000
R_internal = 0.05
T_ambient = 293
V_close = 20
_K_B = 1.38e-23
_SIGMA_SB = 5.67e-08
alpha_abs = 0.3
beta_ext = 0.3
epsilon_t = 0.9
h_b = 10
h_conv = 25
h_pcb = 8
k_cond_bt = 5
k_cond_pt = 3
sigma_jitter = 2e-05
tau_track = 0.1
Initial condition: y(0) = [293, 293, 293, 0, 0, 800, 0, 2e-05]
Horizon: t ∈ [0, 10]

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: high

Default noise: medium

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: HEL Thermal Kill (10kW.AL.800) (hel-thermal-kill-10kw-al-800)

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%	12.7	934	24 ms	0.922
2	SciPy RadauSciPy	100%	12.2	1,011	22 ms	0.910
3	SciPy BDFSciPy	100%	9.9	549	20 ms	0.855
4	CVODE Adamsexternal	100%	9.6	420	12 ms	0.848
5	CVODE BDFexternal	100%	9.6	253	10 ms	0.848
6	SciPy LSODASciPy	100%	9.6	330	3 ms	0.847
7	SciPy DOP853SciPy	100%	9.6	3,050	31 ms	0.847
8	Tsit5external	100%	8.4	2,412	951 ms	0.820
9	SciPy RK45SciPy	100%	8.1	2,174	24 ms	0.812
10	SciPy RK23SciPy	100%	7.8	2,867	36 ms	0.806

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_hel_thermal_kill_10kw_al_800_2026,
  title        = {Resonix Evidence Portal: HEL Thermal Kill (10kW.AL.800)},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonixusa.com/evidence/problems/hel-thermal-kill-10kw-al-800}},
  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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