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Hydrogen fuel cell integration and testing in a hybrid-electric propulsion rig

2023· article· en· W4382401498 on OpenAlexafffund
João Farinha, Luís Miguel Silva, Jay Matlock, Frederico Afonso, Afzal Suleman

Bibliographic record

VenueInternational Journal of Hydrogen Energy · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Victoria
FundersFundação para a Ciência e a TecnologiaCanada Research Chairs
KeywordsStack (abstract data type)PropulsionAutomotive engineeringBattery (electricity)Electrically powered spacecraft propulsionPower (physics)Computer scienceProton exchange membrane fuel cellHydrogen fuelFuel cellsHydrogen vehicleTest benchElectric motorElectric power systemElectric powerHybrid vehicleHybrid systemElectrical engineeringEngineeringAerospace engineeringEmbedded systemPhysicsChemical engineering

Abstract

fetched live from OpenAlex

On the road towards greener aviation, hybrid-electric propulsion systems have emerged as a viable solution. In this paper, a system based on hydrogen fuel cells is proposed and evaluated in a laboratory setting with its future integration in a propulsive system in mind and main focus on the ability to lessen the power demand on the opposing side of the bench. The setup consists in a parallel architecture with two power sources: a hydrogen fuel cell and a battery. First, the performance of the fuel cell and its capability to provide power to one of the motors are analyzed. Then, the entire parallel hybrid system is evaluated. Although the experimental setup was shown to be sub-optimal, the results demonstrated the ability of this greener alternative to reduce power demand on the opposing side of the parallel configuration, with a reduction of up to 40.3% in the highest load scenario, and maximum power output on the fuel cell of 257.8 W. The stack performance was also concluded to be very dependent on the operating temperature.

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.

How this classification was reachedexpand

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.016
GPT teacher head0.259
Teacher spread0.243 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2023
Admission routes2
Has abstractyes

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