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Record W2039348918 · doi:10.1115/1.4005736

Incompressible Laminar Developing Flow in Microchannels

2012· article· en· W2039348918 on OpenAlexaff
Elmer Galvis, Serhiy Yarusevych, J. R. Culham

Bibliographic record

VenueJournal of Fluids Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsReynolds numberLaminar flowMechanicsHydraulic diameterMicrochannelMaterials scienceFlow (mathematics)ThermodynamicsMechanical engineeringPhysicsEngineeringTurbulence

Abstract

fetched live from OpenAlex

In many engineering systems, such as compact heat exchangers and microcoolers used in electronics packaging, system performance depends on laminar flow development in microchannels. This study investigates effects of Reynolds number, hydraulic diameter, and channel aspect ratio on the entrance length in rectangular microchannels. Numerical investigations were performed for microchannels with hydraulic diameters between 100 and 500 μm, Reynolds numbers between 0.5 and 200, and channel aspect ratios between 1 and 5. The results show good agreement with available experimental data and are used to formulate new correlations for estimating the entrance length in microchannels. Compared to other correlations, these new correlations are shown to provide more accurate estimates of entrance length over a wider range of Reynolds numbers representative of practical flows in microchannels.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 communication0.0010.001
Open science0.0000.001
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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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

Citations35
Published2012
Admission routes1
Has abstractyes

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