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Record W1967645990 · doi:10.1080/10893950490477518

ADIABATIC GAS–LIQUID FLOW IN MICROCHANNELS

2004· article· en· W1967645990 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicroscale Thermophysical Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrochannelMaterials scienceSlug flowAdiabatic processPressure dropTwo-phase flowMechanicsFlow (mathematics)Hydraulic diameterThermodynamicsFlow coefficientReynolds numberNanotechnologyPhysics

Abstract

fetched live from OpenAlex

This article presents a review of adiabatic two-phase flow in minichannels and microchannels. Differences between them are identified and explained based on this review and our own research. Several channels of decreasing diameter were used in our experiments to determine the effect of the channel size on the two-phase flow of nitrogen gas and water. The effect of channel geometry was examined by characterizing the two-phase flow in a circular and square microchannel of similar size. Only slug flow was observed in the microchannels. Four new sub-classes of slug flow were subsequently defined. A new correlation was developed for the time-averaged void fraction data in the microchannels. The two-phase pressure drop in microchannels was predicted by treating the two phases as being separate with a large velocity difference. Regarding the effect of microchannel geometry, the transition boundaries on the two-phase flow regime maps were shifted for the slug flow subcategories.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.380
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.190
Teacher spread0.185 · 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