Multi-Node Thermal Network (13-state)
ADVANTAGES2 · dim 13SolvSRK 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 →
13-state multi-node thermal network: battery, 4 motors, 4 ESCs, frame, payload temperatures plus cumulative heat. Motor winding tau ~5s vs battery tau ~500s → stiffness ~100:1. Forced convection from prop wash and airspeed.
Problem definition
Incropera & DeWitt, 'Fundamentals of Heat and Mass Transfer'; Shahid et al., 'Thermal Analysis of Multirotor UAVs' (2022)
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 _mission_profile(t: float) -> tuple[float, float]:
"""Return (motor_current_per_motor_A, airspeed_ms) for each phase."""
if t < 30.0:
return 4.0, 0.5 # spin-up / hover
elif t < 120.0:
return 3.0, 8.0 # cruise
elif t < 180.0:
return 5.5, 2.0 # aggressive hover / filming
elif t < 300.0:
return 3.0, 8.0 # cruise return
elif t < 360.0:
return 3.5, 1.0 # hover descent
else:
return 1.0, 0.0 # idle
def rhs_thermal_envelope(t, y):
"""13-state multi-node thermal network for a quadrotor."""
dy = np.zeros(13)
T_batt = y[0]
T_motors = y[1:5]
T_escs = y[5:9]
T_frame = y[9]
T_payload = y[10]
I_motor, airspeed = _mission_profile(t)
I_pack = I_motor * 4
# Convective enhancement from airspeed (forced convection scaling)
conv_factor = 1.0 + 2.0 * math.sqrt(max(0, airspeed))
# Battery heat generation (I^2 R)
P_batt = I_pack**2 * _BATT_R_INTERNAL
# Motor heat generation (copper losses, per motor)
P_motors = np.array([I_motor**2 * _MOTOR_R_PHASE] * 4)
# Add iron losses (proportional to speed, approximated)
P_motors += 0.3 * I_motor
# ESC heat generation (conduction + switching, per ESC)
P_escs = np.array([I_motor**2 * _ESC_R_DS_ON * 2 + 0.1 * I_motor] * 4)
# Battery thermal dynamics
Q_batt_to_frame = (T_batt - T_frame) / _R_BATT_FRAME
Q_batt_conv = _H_BATT_BASE * conv_factor * (T_batt - _T_AMB)
dy[0] = (P_batt - Q_batt_to_frame - Q_batt_conv) / _C_BATT
# Motor thermal dynamics (4 motors)
for i in range(4):
Q_motor_frame = (T_motors[i] - T_frame) / _R_MOTOR_FRAME
Q_motor_conv = _H_MOTOR_BASE * conv_factor * (T_motors[i] - _T_AMB)
dy[1 + i] = (P_motors[i] - Q_motor_frame - Q_motor_conv) / _C_MOTOR
# ESC thermal dynamics (4 ESCs)
for i in range(4):
Q_esc_frame = (T_escs[i] - T_frame) / _R_ESC_FRAME
Q_esc_conv = _H_ESC_BASE * conv_factor * (T_escs[i] - _T_AMB)
dy[5 + i] = (P_escs[i] - Q_esc_frame - Q_esc_conv) / _C_ESC
# Frame thermal dynamics (receives heat from all components)
Q_in_frame = (
(T_batt - T_frame) / _R_BATT_FRAME +
sum((T_motors[i] - T_frame) / _R_MOTOR_FRAME for i in range(4)) +
sum((T_escs[i] - T_frame) / _R_ESC_FRAME for i in range(4))
)
Q_frame_air = (T_frame - _T_AMB) / _R_FRAME_AIR
Q_frame_payload = (T_frame - T_payload) / _R_PAYLOAD_FRAME
dy[9] = (Q_in_frame - Q_frame_air - Q_frame_payload) / _C_FRAME
# Payload thermal dynamics
dy[10] = (T_frame - T_payload) / (_R_PAYLOAD_FRAME * _C_PAYLOAD)
# Cumulative heat tracking
dy[11] = P_batt
dy[12] = P_batt + float(np.sum(P_motors)) + float(np.sum(P_escs))
return dy- Parameters
- _BATT_R_INTERNAL = 0.06
- _C_BATT = 120
- _C_ESC = 2
- _C_FRAME = 50
- _C_MOTOR = 8
- _C_PAYLOAD = 15
- _ESC_R_DS_ON = 0.008
- _H_BATT_BASE = 0.1
- _H_ESC_BASE = 0.3
- _H_MOTOR_BASE = 0.5
- _MOTOR_R_PHASE = 0.15
- _R_BATT_FRAME = 8
- _R_ESC_FRAME = 6
- _R_FRAME_AIR = 15
- _R_MOTOR_FRAME = 4
- _R_PAYLOAD_FRAME = 10
- _T_AMB = 25
- Initial condition
- y(0) = [25, 25, 25, 25, 25, 25, …] [shape=(13,), min=0, max=25]
- 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: low
Default noise: low
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: Multi-Node Thermal Network (13-state) (multi-node-thermal-network-13-state)
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% | 12.1 | 4,410 | 70 ms | 0.907 |
| 2 | SciPy RadauSciPy | 100% | 8.9 | 5,571 | 118 ms | 0.831 |
| 3 | SciPy LSODASciPy | 100% | 8.0 | 1,680 | 20 ms | 0.810 |
| 4 | SciPy DOP853SciPy | 100% | 7.9 | 3,650 | 74 ms | 0.806 |
| 5 | SciPy BDFSciPy | 100% | 7.6 | 2,139 | 62 ms | 0.801 |
| 6 | CVODE BDFexternal | 100% | 7.3 | 1,493 | 37 ms | 0.793 |
| 7 | CVODE Adamsexternal | 100% | 7.2 | 1,215 | 31 ms | 0.791 |
| 8 | SciPy RK23SciPy | 100% | 7.0 | 3,479 | 82 ms | 0.785 |
| 9 | SciPy RK45SciPy | 100% | 6.6 | 2,432 | 30 ms | 0.776 |
| 10 | Tsit5external | 100% | 6.2 | 3,024 | 673 ms | 0.767 |
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_multi_node_thermal_network_13_state_2026,
title = {Resonix Evidence Portal: Multi-Node Thermal Network (13-state)},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonixusa.com/evidence/problems/multi-node-thermal-network-13-state}},
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