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Record W4403188257 · doi:10.3390/modelling5040074

Acausal Fuel Cell Simulation Model for System Integration Analysis in Early Design Phases

2024· article· en· W4403188257 on OpenAlex

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.

Bibliographic record

VenueModelling—International Open Access Journal of Modelling in Engineering Science · 2024
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsConcordia University
FundersPolitecnico di Torino
KeywordsSystems engineeringComputer scienceEnvironmental scienceEngineeringNuclear engineering

Abstract

fetched live from OpenAlex

Hydrogen technologies have the potential to reduce aviation’s CO2 emissions but come with many challenges. This paper introduces a scalable hydrogen fuel cell model tailored for system integration analysis in early aircraft design phases. The model focuses on Proton Exchange Membrane Fuel Cells (PEMFCs) and is based on thermodynamic equations and empirical data to simulate performance under different ambient and operating conditions; it also includes a simplified model of the Balance of Plant (BOP) systems and is implemented in OpenModelica. The model performance is validated through a comparison of the simulated polarization curves with real datasheet data. A case study highlights the peculiarities of this model by studying the sizing of the fuel cell stacks for a modified ATR 72 aircraft. The developed model effectively supports the early design exploration of the aircraft with a greater level of detail for system integration studies, essential to better explore the potential of aircraft featuring hydrogen-based power systems.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.859
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0020.004
Open science0.0020.000
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 head0.081
GPT teacher head0.343
Teacher spread0.262 · 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