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Record W2921949718 · doi:10.1115/1.4043160

Analytical Solution of Water Vapor Condensation in Flow Channel of Battery Pack

2019· article· en· W2921949718 on OpenAlexaff
HongGuang Sun, Chih-Cheng Hsu

Bibliographic record

VenueJournal of Heat Transfer · 2019
Typearticle
Languageen
FieldEngineering
TopicFire Detection and Safety Systems
Canadian institutionsGeneral Motors (Canada)
Fundersnot available
KeywordsLaminar flowCondensationEvaporationMechanicsAspect ratio (aeronautics)Flow (mathematics)Channel (broadcasting)ThermodynamicsHydraulic diameterVolumetric flow rateMaterials scienceReynolds numberPhysicsEngineeringTurbulenceComposite materialElectrical engineering

Abstract

fetched live from OpenAlex

The analytical solutions for condensation (and evaporation) rates of laminar humid air flow in the channels of a battery pack at both entrance and downstream regions are obtained/modeled. The effects of the entrance fluid velocity profile and the aspect ratio of the flow channel are taken into consideration. Initially, an analytical solution for laminar humid air flows' condensation at the flow channel with infinite aspect ratio and fully developed flow profile at the entrance is obtained. The solution is in good agreement with the simulation result obtained from a correlated fluent condensation model. After performing simulations of water vapor condensation and evaporation in the flow channel for different aspect ratios and entrance velocity profiles, the analytical solutions of condensation and evaporation rate of water vapor are presented in analytic forms by adding appropriate coefficients/correction factors to the solution for the flow in the channel with infinite aspect ratio. The resulting model accurately captures the effects of the entrance velocity profile and aspect ratio.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.197
Teacher spread0.187 · 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 designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

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