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Record W2060207450 · doi:10.1115/fedsm-icnmm2010-30417

Analytical and Experimental Characterization of Flow in Slowly-Varying Cross-Section Microchannels

2010· article· en· W2060207450 on OpenAlexafffund
Mohsen Akbari, Majid Bahrami, David Sinton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversity of VictoriaSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrochannelPressure dropLaminar flowMechanicsCross section (physics)Materials scienceFlow (mathematics)Hydraulic diameterDrop (telecommunication)PhysicsMechanical engineeringEngineeringReynolds number

Abstract

fetched live from OpenAlex

This paper outlines a novel approximate solution for determining the pressure drop of laminar, single-phase flow in slowly-varying microchannels of arbitrary cross-section. The proposed analysis is general and applicable to symmetric and asymmetric microchannel cross-sections, as examples compact relationships are reported for elliptical and rectangular shapes for three common wall profiles of linear, sinusoidal and hyperbolic. An experimental setup is designed and pressure drop measurements are conducted to validate the proposed model for streamwised periodic microchannels with rectangular cross-section and linear wall with a range of channel geometrical parameters such as aspect ratio and channel slope. The model is also compared against the numerical and experimental data of hyperbolic contraction with rectangular cross-section collected by others. It is observed that although the proposed model is based on the solution of the elliptical cross-section, it can accurately predict the pressure drop in microchannels of rectangular cross-section.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.000
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 head0.008
GPT teacher head0.241
Teacher spread0.232 · 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 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

Citations7
Published2010
Admission routes2
Has abstractyes

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