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Record W3092273012 · doi:10.11159/enfht20.117

Investigation of Sequential and Simultaneous Crossflow HeatExchangers for Automotive Application

2020· article· en· W3092273012 on OpenAlexafffund
Mohammed Ismail, Mesbah G. Khan, Amir Fartaj

Bibliographic record

VenueProceedings of the World Congress on Momentum, Heat and Mass Transfer · 2020
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Windsor
KeywordsAutomotive industryHeat exchangerAutomotive engineeringComputer scienceMechanical engineeringEnvironmental scienceMaterials scienceEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

In current research, forced convective heat transfer of sequential and simultaneous heat exchangers are numerically investigated. Both, the sequential and the simultaneous, modules of heat exchangers are identical in size, i.e. frontal area and volume. The simulations have been conducted on serpentine finned heat exchangers in air-to-liquid cross-flow orientation using ANSYS FLUENT, a widely used finite volume method (FVM) commercial code. The heat transfer are concurrently obtained for automatic transmission fluid (ATF) and 50% ethylene glycol-water mixture (EG). In the airside, the constant inlet temperature and velocity of the air have been maintained at 25C and 6.3 m/s respectively. In liquid side, the inlet temperature of ATF and EG have also been kept constant at 150C and 105C respectively. For both the sequential and the simultaneous orientations, air has been used to cool ATF and EG at various massflow rates within a laminar flow regime. For a given Reynolds number, simultaneous heat exchanger module displays significant enhancement of heat transfer rate than that of the conventional sequential module.

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 score0.161
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.014
GPT teacher head0.214
Teacher spread0.200 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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