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Record W2801702573 · doi:10.1115/icmm2004-2359

Effect of Channel Diameter and Liquid Property on Void Fraction in Adiabatic Two-Phase Flow Through Microchannels

2004· article· en· W2801702573 on OpenAlexafffund
Masahiro Kawaji, Akimaro KAWAHARA, Peter M.-Y. Chung, Michio SADATOMI, Kazuya Okayama

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of Toronto
FundersJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePorosityHydraulic diameterAdiabatic processSurface tensionVoid (composites)Two-phase flowAnalytical Chemistry (journal)MechanicsThermodynamicsComposite materialChromatographyFlow (mathematics)ChemistryReynolds numberPhysics

Abstract

fetched live from OpenAlex

Adiabatic two-phase flow experiment have been conducted to investigate the effects of channel diameter and liquid property on void fraction in horizontal microchannels. Water/nitrogen gas and ethanol-water/nitrogen gas mixtures were pumped through circular microchannels of 50, 75, 100 and 251 μm diameter. The concentration of ethanol in water was varied to change the surface tension and liquid viscosity. The void fraction data were obtained by an image analysis technique and correlated as a function of homogeneous void fraction. The void fraction data in channels with a diameter between 50 and 100 μm conformed well to Kawahara et al.’s (2002) correlation, but the data for a 251 μm diameter channel agreed with the Armand correlation (1946) suitable for minichannels. There was no significant effect of liquid properties on void fraction for all the channel sizes. Thus, these results suggest that the boundary between microchannels and minichannels would lie between 100 and 251 μm, and not be sensitive to the fluid property.

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: Bench or experimental · Consensus signal: Bench or experimental
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 studies0.0000.000
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 head0.010
GPT teacher head0.266
Teacher spread0.256 · 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

Citations2
Published2004
Admission routes2
Has abstractyes

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