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Record W2195309545 · doi:10.1115/icnmm2013-73096

Effect of Tube Diameters on the Flow Phenomena of Gas-Liquid Two-Phase Flow in Microchannels

2013· article· en· W2195309545 on OpenAlexaff
Hideo Ide, Masahiro Kawaji

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrochannelSlug flowMaterials scienceFlow (mathematics)Two-phase flowMicrofluidicsPressure dropTube (container)MechanicsHeat exchangerComposite materialMechanical engineeringNanotechnologyEngineeringPhysics

Abstract

fetched live from OpenAlex

The studies on microfluidics have advanced with the use of microchannels in micro bioreactors, applications in biomedical engineering, bioengineering and pharmaceuticals, fuel cells and compact heat exchangers for heating and cooling of micro electro mechanical systems. For a wide application of microchannels, it is very important to elucidate the effect of the tube diameters on the flow phenomena of gas liquid two-phase flow in microchannels. The flow phenomena and the frictional pressure drop of gas-liquid two-phase flow were investigated experimentally by using three kinds of circular microchannels made of fused silica tubes, with inner diameters of 0.1 mm, 0.15 mm and 0.25mm. In these channels, bubbly flow, slug-churn flow, and annular flow were observed. In annular flow, two characteristic flows of ring film flow and disturbed ring film flow were observed. For the 0.25 mm diameter microchannel, several conductance probe sensors were flush mounted to the channel wall in order to measure the very thin mean film thickness or liquid holdup in a microchannel. The flow patterns maps were also made by using the high speed video images and the holdup wave signals. The effects of tube diameter on the flow patterns were investigated.

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 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 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 score0.063
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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 teacher head, 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

Citations3
Published2013
Admission routes1
Has abstractyes

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