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Record W1958839546 · doi:10.1504/ijpse.2015.071431

Automotive underbody diffuser for battery thermal management

2015· article· en· W1958839546 on OpenAlexaff
Rocky Khasow, Martin Agelin Chaab

Bibliographic record

VenueInternational Journal of Process Systems Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsDiffuser (optics)Battery packBattery (electricity)Automotive engineeringAerodynamic dragRange (aeronautics)AerodynamicsAir coolingThermalAutomotive industryEngineeringMarine engineeringEnvironmental scienceElectrical engineeringMechanical engineeringAerospace engineeringMeteorologyPhysicsPower (physics)

Abstract

fetched live from OpenAlex

This paper investigates underbody aero-thermal management of a hypothetical battery pack. Underbody diffusers are specifically designed to channel air for cooling a surface of the battery pack without significantly increasing aerodynamic drag. Numerical simulations are conducted to study the cooling and drag effects of the new diffusers on the battery pack. The numerical results show that the temperature of the battery pack upstream decreased whereas that at the downstream slightly increased compared to the no diffuser case, in addition to having a larger range of temperatures. There are smaller hot spots in comparison to the no diffuser case, which limit the number of cells in a battery that would be affected by the temperature increase, thus preventing damage. With further studies and improved diffuser design, the present work has the potential to offer better alternative locations for installing EV and HEV battery packs for improved air cooling.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.291
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), 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
Published2015
Admission routes1
Has abstractyes

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