Multiphysics Optimization of Thermal Management Designs for Power Electronics Employing Impingement Cooling and Stereolithographic Printing
Bibliographic record
Abstract
Meeting the stringent performance requirements for power electronic converters in electric vehicles requires an integrated approach for optimizing the inherently coupled electrical and thermal performances of converter systems. This article presents a multidisciplinary thermal management design methodology that utilizes genetic algorithms (GAs) to generate topologically optimized geometries for liquid-cooled heat sinks. These GA-generated heat sinks are based on impingement cooling principles and leverage the flexibility of stereolithographic manufacturing techniques. The proposed optimization methodology incorporates the interdependence between the thermal and electrical aspects of the system, and it is capable of targeting performance metrics in either or both domains. This optimization process is demonstrated for a 6.6-kW integrated power module design employing bare-die silicon carbide devices on an FR4-based printed circuit board with embedded ceramic elements. Experimentally validated electrothermal multiphysics simulations of the GA-optimized heat sinks targeting various performance metrics show successful optimization of targeted metrics relative to the initial seed design. The results demonstrate the importance of the multidisciplinary design approach and the effectiveness of the GA-based optimization methodology.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".