A Study on Thermal Management Systems for Fuel-Cell Powered Regional Aircraft
Bibliographic record
Abstract
This work studies the feasibility of integrating a hydrogen-powered propulsion system in a regional aircraft at the conceptual design level. The developed system consists of fuel cells, which will be studied at three technological levels, and batteries, also studied for four hybridization factors (X = 0, 0.05, 0.10, 0.20). Hydrogen can absorb great thermal loads since it is stored in the tank at cryogenic temperatures and is used as fuel in the fuel cells at around 80 °C. Taking advantage of this characteristic, two thermal management system (TMS) architectures were developed to ensure the proper functioning of the aircraft during the designated mission: A1, which includes a vapor compression system (VCS), and A2, which omits it for a simpler design. The models were developed in MATLAB® and consist of different components and technologies commonly used in such systems. The analysis reveals that A2, due to the exclusion of the VCS, outperformed A1 in weight (10–23% reduction), energy consumption, and drag. A1’s TMS required significantly more energy due to the VCS compressor. Hybridization with batteries increased system weight substantially (up to 37% in A2) and had a greater impact on energy consumption in A2 due to additional fan work. Hydrogen’s heat sink capacity remained underutilized, and the hydrogen tank was deemed suitable for a non-integral fuselage design. A2 had the lowest emissions (10–20% lower than A1 for X = 0), but hybridization negated these benefits, significantly increasing emissions in pessimistic scenarios.
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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 | 0.001 | 0.001 |
| 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".