A Study on Thermal Management Systems for Hybrid–Electric Aircraft
Bibliographic record
Abstract
The electrification of an aircraft’s propulsive system is identified as a potential solution towards a lower carbon footprint in the aviation industry. One of the effects of increased electrification is the generation of a large amount of waste heat that needs to be removed. As high-power systems must be cooled to avoid performance deterioration such as battery thermal runaway, a suitable thermal management system is required to regulate the temperature of the powertrain components. With this in mind, the main objective of this research is to identify promising heat transfer technologies to be integrated into a thermal management system (TMS) such that power, mass, and drag can be minimised for a parallel hybrid–electric regional aircraft in the context of the EU-funded FutPrInt50 project. Five different TMS architectures are modelled using the Matlab/Simulink environment based on thermodynamic principles, heat transfer fundamentals, and fluid flow equations. The systems are a combination of a closed-loop liquid cooling integrated with different heat dissipation components, namely ram air heat exchanger, skin heat exchanger, and fuel. Their cooling capacity and overall aircraft performance penalties under different flight conditions are estimated and compared to each other. Then, a parametric study is conducted, followed by a multi-objective optimisation analysis with the aim of minimising the TMS impact. As expected, none of the investigated architectures exhibit an ideal performance across the range of the studied metrics. The research revealed that, while planning the TMS for future hybrid–electric aircraft, alternative architectures will have to be developed and studied in light of the power requirements.
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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 | 0.001 |
| 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) | 0.002 | 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".