heatsink_forced_mlp

PINNeAPPle ModelRegistry 'bench_mlp' (4 x 128, SiLU) on log-scaled geometry, material and operating variables, trained on 6000 solves of the HeatSink Sizer physics engine (Teertstra composite parallel-plate model, 3-D finite-volume base conduction, developing-flow pressure drop). Returns the resistance, the pressure drop and a split-conformal 95 % upper bound on the resistance.

heatsink_forced_mlp v1.0.0 (revision v1.0.0.post1) · part of the PINNeAPPle zoo · training data PINNeAPPle-Labs/heatsink-forced-convection

Try it without installing anything: PINNeAPPle zoo demo (pick heatsink_forced_mlp: its predictions along each input with their band and where its physics checks warn or fail, and your own inputs evaluated in the browser).

Task: Plate-fin heat sink in ducted forced convection: base-to-air thermal resistance and pressure drop

Inputs

Outputs

Validity range

• base_width_mm: [30.0, 200.0]
• base_depth_mm: [30.0, 200.0]
• base_thickness_mm: [2.0, 15.0]
• fin_thickness_mm: [0.6, 4.0]
• fin_height_mm: [8.0, 80.0]
• k_w_mk: [150.0, 400.0]
• source_frac_w: [0.15, 1.0]
• source_frac_d: [0.15, 1.0]
• t_ambient_c: [0.0, 50.0]
• fin_gap_mm: [0.6, 12.0]
• fin_gap_note: gap sampled in 0.8-10 mm, then the fin count is rounded; the realised gaps of the training designs span 0.56-12.2 mm
• air_velocity_m_s: [0.5, 6.0]
• power_w: [10.0, 400.0]
• regime: ducted (no bypass) laminar/developing forced convection in air

Known limitations

• Ducted flow: real heat sinks with bypass lose 20-50 % of the airflow; correct the velocity first.
• Convection correlations carry a typical +-15 % accuracy; the surrogate's own error (<1 % mean) is against the engine, not against a measurement.
• Screening tool: re-verify a chosen design with the physics engine.

Architecture

• bench_mlp — {"in_dim": 13, "out_dim": 2, "width": 128, "depth": 4, "act": "silu", "targets": ["log R", "log dP"]}

Physics

• Teertstra, Yovanovich & Culham (2000) composite parallel-plate Nusselt number
• 3-D finite-volume conduction in the base (spreading resistance)
• developing-flow friction + entry/exit losses for the pressure drop

Validation

Reference: 900 held-out designs solved by the HeatSink Sizer physics engine (pinneapple_design.thermal.heatsink.engine). The engine is checked against independent methods: Lee et al. spreading (1-5 %), Stephan developing-flow Nu + e-NTU (0-5 %), fully developed friction (2-4 %), FVM grid convergence < 2 %.

Trust

Decision: APPROVED · score 1.00 · coverage 100%

Training data

• dataset: heatsink_forced_convection
• source: apps/heatsink_sizer/artifacts/{surrogate_forced.pt,dataset_forced.json.gz}

License: apache-2.0

Citation

PINNeAPPle contributors (2026). HeatSink Sizer forced-convection surrogate. PINNeAPPle model zoo, https://github.com/PINNeAPPle-Labs/PINNeAPPle

Physics validation

pass: every applicable check passed (22/22 applicable checks pass; level standard; generated by pp.validate with PINNeAPPle 0.7.0, schema validation-report/v1).

Model SHA-256 2ecc1bdef8460f1ce95dc92eb1f24ecdbc701b27ab4ab954a40087501e3789fb.

How to use

pip install "pinneapple @ git+https://github.com/PINNeAPPle-Labs/PINNeAPPle"

import pinneapple as pp

model = pp.zoo.load("heatsink_forced_mlp")          # weights checked against their SHA-256
inputs = {"base_width_mm": 44.7278, "base_depth_mm": 138.196, "base_thickness_mm": 3.54108, "n_fins": 15.0, "fin_thickness_mm": 0.81635, "fin_height_mm": 15.0093, "k_w_mk": 183.817, "source_width_mm": 13.5681, "source_depth_mm": 21.6939, "t_ambient_c": 27.7717, "air_velocity_m_s": 4.20166, "power_w": 297.703}
out = model.predict(inputs)                 # warns for inputs outside the validity range
print({k: round(float(v[0]), 6) for k, v in out.items()})
# {'r_base_k_w': 0.882924, 'pressure_drop_pa': 66.151017, 'r_base_upper95_k_w': 0.901249}
print(pp.zoo.query_checks(model, inputs)[0]["verdict"]["status"])   # per-query trust checks
# pass
print(pp.zoo.validate(model).decisao)        # card metrics reproduced + physics checks
# APPROVED

The inputs are the first held-out test case of the training dataset; its reference value is r_base_k_w = 0.874388, pressure_drop_pa = 65.4357. pp.zoo.evaluate("heatsink_forced_mlp") recomputes every metric of the card on the stored reference.

Hosted inference

No Hugging Face inference provider can run this model, so the Hub widget is off (inference: false). It is a small PyTorch network, but its prediction is more than a forward pass: the inputs are encoded and normalised, the outputs denormalised (and, for ensembles, averaged with their spread), and every query is checked against the validity range and the model's physics checks. The pinneapple library does all of that (below); the ONNX graph carries the pre- and post-processing but not the checks; the zoo demo Space shows the model along a sweep of each input and runs it, with its checks, in your browser.

ONNX

model.onnx (opset 17) maps x, shape (n, 12) float32 with the columns base_width_mm, base_depth_mm, base_thickness_mm, n_fins, fin_thickness_mm, fin_height_mm, k_w_mk, source_width_mm, source_depth_mm, t_ambient_c, air_velocity_m_s, power_w, to the outputs y: ['r_base_k_w', 'pressure_drop_pa', 'r_base_upper95_k_w']. Pre- and post-processing are in the graph (largest difference from PyTorch on the reference inputs: 1.8e-06); the validity-range and physics checks are not, so check the range yourself.

import numpy as np, onnxruntime as ort
from huggingface_hub import hf_hub_download

path = hf_hub_download("PINNeAPPle-Labs/heatsink-forced-mlp", "model.onnx", revision="v1.0.0.post1")
session = ort.InferenceSession(path)
x = np.array([[44.7278, 138.196, 3.54108, 15.0, 0.81635, 15.0093, 183.817, 13.5681, 21.6939, 27.7717, 4.20166, 297.703]], np.float32)
print(dict(zip([o.name for o in session.get_outputs()], session.run(None, {"x": x}))))

Files

The SHA-256 are those of the package's manifest: pp.zoo.load refuses any other file.

Cite

If you use this model, please cite PINNeAPPle (software, Zenodo DOI 10.5281/zenodo.23343062; see CITATION.cff):

@software{pinneapple,
  author = {Barros, Yan and Schiassi, Enrico},
  title  = {PINNeAPPle},
  doi    = {10.5281/zenodo.23343062},
  url    = {https://github.com/PINNeAPPle-Labs/PINNeAPPle}
}