Topology Optimization of Passively Cooled Heatsinks for High Efficiency Electronics
Bibliographic record
Abstract
Density-based topology optimization techniques are applied to the design of heatsinks for passively-cooled electronics. Existing methodologies are utilized to minimize the thermal compliance for high-resolution large-scale three-dimensional heatsinks using a comprehensive and extensible computational framework. High-fidelity and low-fidelity fluid flow models are considered for accuracy and computational efficiency, respectively. An efficient verification model is proposed to accurately quantify the final performance of the optimized heatsink designs. The results are compared to traditionally-optimized parallel plate-fin heatsink reference designs. Various studies are performed to determine the optimum combination of parameters for generating results. The optimization results proved to exceed the performance of the reference designs with equivalent minimum feature size by 14% for thermal compliance with a 67% reduction in volume. When considering a reference design that does not meet the minimum feature size, the optimization results proved to lower the thermal compliance by 7% with a 48% reduction in volume.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".