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Record W2071580039 · doi:10.4271/2014-01-1181

Heat and Mass Flow Characterization of Highly Viscous Fluid in Narrow-Channel Heat Exchanger

2014· article· en· W2071580039 on OpenAlexafffund
Md. Abdul Quaiyum, Mohammed Ismail, Amir Fartaj

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2014
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Windsor
KeywordsHeat exchangerMechanicsFlow (mathematics)Materials scienceCharacterization (materials science)Channel (broadcasting)Micro heat exchangerOpen-channel flowFluid dynamicsPetroleum engineeringThermodynamicsPlate heat exchangerComputer scienceGeologyPhysicsTelecommunicationsNanotechnology

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Channel diameter is one of the most important parameters of a heat exchanger especially for a highly viscous fluid-flow. Narrow channel heat exchangers are believed to have better energy efficiency due to elevated heat transfer characteristics. Heat transfer and Fluid-flow behaviors of Automatic Transmission Fluid (<i>ATF</i>) have been experimentally investigated in a closed loop integrated thermal wind tunnel test facility using wavy finned Minichannel Heat Exchanger (<i>MICHX</i>). The experiment was conducted by varying the <i>ATF</i> Reynolds number from 3 to 30. The flow friction factors in minichannel were evaluated. For a fully developed laminar flow the friction factors were evaluated considering fluid viscosity effects due to temperature variation. The flow correlated with a Poiseuille equation while friction factors were analyzed considering constant property ratio. However, it showed different correlation when considered variable property ratio. A numerical analysis on friction factor for single serpentine <i>MICHX</i> did not follow the Poiseulle law for both cases of constant property ratio and variable property ratio. The analysis showed significantly higher <i>fRe</i> values. The friction factor demonstrated strong dependency on log mean temperature difference, Reynolds number, and capacity rate ratio as expected.</div></div>

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 categoriesMeta-epidemiology (narrow)
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 score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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.

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

Citations0
Published2014
Admission routes2
Has abstractyes

Explore more

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