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Record W4411217978 · doi:10.3390/en18123074

A Study on Thermal Management Systems for Fuel-Cell Powered Regional Aircraft

2025· article· en· W4411217978 on OpenAlex
M L Figueiredo Filipe, Frederico Afonso, Afzal Suleman

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnergies · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsUniversity of Victoria
FundersFundação para a Ciência e a TecnologiaNatural Sciences and Engineering Research Council of Canada
KeywordsFuel cellsThermal management of electronic devices and systemsThermalAerospace engineeringAutomotive engineeringAeronauticsEnvironmental scienceEngineeringSystems engineeringNuclear engineeringMeteorologyMechanical engineeringChemical engineeringPhysics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.250
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it