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Record W4405498984 · doi:10.2514/1.c038205

Passive Thermal Management of an Electric Trainer Aircraft Battery Pack Considering Aging

2024· article· en· W4405498984 on OpenAlexafffund
Émile Veilleux, Hadi Pasdarshahri, David Rancourt, Doriane Ibtissam Hassaine Daoudji

Bibliographic record

VenueJournal of Aircraft · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsBattery packTrainerBattery (electricity)Automotive engineeringAeronauticsThermal management of electronic devices and systemsAerospace engineeringEngineeringMarine engineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

This paper investigates the use of a passive in-flight battery thermal management system for an electric trainer aircraft. The proposed architecture employs a high-conductivity heat pipe coupled with a heat sink to provide cooling. Two different thermal management approaches are used for ground and flight operations. During charge, a ground-based air conditioning unit is used for thermal management. In flight, the system relies on preconditioning of the battery pack’s thermal mass to limit the temperature rise to acceptable levels, reducing the onboard weight and complexity associated with the battery thermal management system. To assess the performance of the proposed system over the entire lifetime of the battery pack, for both flight and ground operations, a coupled electrothermal-aging model based on semi-empirical data was created and validated experimentally for a notional touch-and-go training mission of a conceptual electric Cessna 172N. Resulting time-dependent performance diagrams presenting the evolution of maximum battery temperature, the ground turnaround time, and the capacity fade are used to determine operational constraints throughout the 2-year-and-a-half lifetime of the pack.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.714

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.268
Teacher spread0.254 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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