Optimization of Pin Arrangement and Geometry in EV and HEV Heat Sink Using Genetic Algorithm Coupled With CFD
Bibliographic record
Abstract
The development of high-power electronic devices applied to various energy systems has recently gained a great deal of attention and specifically for electric vehicles (EVs) or hybrid electric vehicles (HEVs). However, the continued miniaturization and increased power output of power electronic have also introduced several design challenges especially in thermal management. In this paper, a genetic algorithm (GA) is used as a design tool to optimize both the pin arrangement and pin geometry of a pin fin heat sink with localized heat sources representing an EV power driver. Computational fluid dynamics (CFD) simulation is used in the GA to evaluate the performance of each potential design during optimization. The GA optimizes a performance index which captures both the pressure drop across the heat exchanger and the thermal efficiency with the two conflicting objectives, the Colburn factor$j$and the friction factor f. A sets of optimization with different geometric parameters have been carried out and compared. The results demonstrate that GA coupled with CFD may be used to create designs that have better heat transfer coefficient and less pressure requirement than traditional pin fin arrangement designs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".