Experimental Investigation of Enhanced Heat Sinks for Hybrid Electric Aircraft
Bibliographic record
Abstract
Abstract Controlling the temperature of onboard battery systems is a key thermal management challenge for hybrid electric aircrafts. Initially, a standard plate-fin heat sink was used in an innovative approach to dissipate heat generated by battery cells in electric vehicles. However, based on an analysis of a kW-scale hybrid electric aircraft, two heat sinks were specifically optimized for the aircraft’s application: pin-fin and micro-structured heat sinks. This paper compares the performance of these optimized heat sinks with that of the original plate-fin heat sink. A custom-designed box was created to direct airflow into the heat sinks, modeled in accordance with the specifications used in CFD analysis. Testing was conducted across two different airflow rate ranges. The results showed that the optimized heat sinks outperform the original in terms of thermal performance and weight. Experimental results indicated a more substantial performance enhancement with the pin-fin heat sink than what the simulations predicted. Significant improvements were observed using optimized pin-fin heat sink at higher airflow rates and greater source powers. At lower airflow rates, the microstructured heat sink, produced through advanced micromachining, proved to be the optimal choice, delivering the best performance with the lightest weight.
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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 | 0.000 |
| 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 | 0.000 | 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".