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Record W2267717655 · doi:10.36884/jafm.3.02.11886

Slip-Flow and Heat Transfer in Isoflux Rectangular Microchannels with Thermal Creep Effects

2010· article· en· W2267717655 on OpenAlexfundno aff
Hamid Niazmand, Ali Amiri Jaghargh, Metin Renksizbulut

Bibliographic record

VenueJournal of Applied Fluid Mechanics · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFerdowsi University of Mashhad
KeywordsCreepMechanicsReynolds numberMicrochannelHeat fluxMaterials scienceSlip (aerodynamics)Heat transferRarefaction (ecology)Heat transfer coefficientSlip ratioThermodynamicsThermalComposite materialPhysicsTurbulenceGeologyShear stress

Abstract

fetched live from OpenAlex

A control-volume numerical approach has been used to study rarefaction effects in simultaneously hydrodynamically and thermally developing flow in rectangular microchannels with a prescribed uniform wall heat flux in the slip-flow regime (10 -3 Kn 10 -1 ). The effects of velocity slip and thermal creep on the key flow parameters are examined in detail. Low Reynolds number flows (Re 1) for different channel aspect ratios (0 * 1) are considered. The effects of rarefaction on the global features of the flow and thermal development in the entrance region are examined. Dramatic reductions in the friction coefficient are observed in the entrance region due to rarefaction effects, which are enhanced by thermal creep. For the fluid heating cases considered here, thermal creep increases slip at the wall and thereby further reduces the friction coefficient and slightly enhances heat transfer at a given Reynolds number. For an identical heat flux applied to the microchannel walls, thermal creep effects become much more pronounced at lower Reynolds numbers since it results in higher axial temperature gradients.

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 score0.002
Threshold uncertainty score0.004

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)0.0000.000

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 head0.002
GPT teacher head0.155
Teacher spread0.153 · 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

Citations15
Published2010
Admission routes1
Has abstractyes

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