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Record W1996370191 · doi:10.1142/s012918311350054x

ENTRANCE EFFECTS OF THERMAL CREEP ON FLUID HEATING IN RECTANGULAR MICROCHANNELS

2013· article· en· W1996370191 on OpenAlexaff
Ali Amiri-Jaghargh, Hamid Niazmand, Metin Renksizbulut

Bibliographic record

VenueInternational Journal of Modern Physics C · 2013
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversity of Waterloo
FundersFerdowsi University of Mashhad
KeywordsKnudsen numberTemperature jumpMechanicsMaterials scienceHeat transferSlip (aerodynamics)CreepReynolds numberSlip ratioThermodynamicsWork (physics)ThermalMicrochannelComposite materialPhysicsShear stressTurbulence

Abstract

fetched live from OpenAlex

The effects of thermal creep on the development of gaseous fluid flow and heat transfer in rectangular microchannels with constant wall temperature are investigated in the slip-flow regime. Thermal creep arises from tangential temperature gradients, which may be significant in the entrance region of channels, and affects the velocity and temperature fields particularly in low Reynolds number flows. In the present work, the Navier–Stokes and energy equations coupled with velocity-slip and temperature-jump conditions applied at the channel walls are solved numerically using a control-volume technique. Despite the constant wall temperature, tangential temperature gradients form in the gas layer adjacent to the wall due to the temperature-jump condition. The effects of slip/jump and thermal creep on the flow patterns and parameters are studied in detail for a wide range of channel aspect ratios and, Knudsen and Reynolds numbers. Furthermore, the effects of variable properties on velocity-slip and, friction and heat transfer coefficients are also examined.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.206
Teacher spread0.201 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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Same venueInternational Journal of Modern Physics CSame topicHeat Transfer and OptimizationFrench-language works237,207