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Record W2521222931

Effect of contact angle on gas slug formation, shape and flow in a microchannel T-junction by numerical simulation

2010· article· en· W2521222931 on OpenAlexaff
Rafael M. Santos, Masahiro Kawaji

Bibliographic record

VenueLirias (KU Leuven) · 2010
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrochannelContact angleMechanicsSlug flowSlip (aerodynamics)Materials scienceBubbleSlip ratioTwo-phase flowChemistryFlow (mathematics)ThermodynamicsComposite materialShear stressPhysics
DOInot available

Abstract

fetched live from OpenAlex

The effect of the contact angle on two-phase flow slug formation in a microchannel T-junction was studied by numerical simulation. The contact angle, varied from 0o to 120o, determined the interaction of the gas and liquid phases with the channel wall, affecting their shape, size and velocity. The interface shape was also found to vary due to the Laplace pressure, which is a function of the contact angle and the slug velocity. The visualisation of the cross-sectional area of gas slugs allowed for insight into the existence of liquid flow along rectangular microchannel corners, which was affected by the contact angle value and determined the occurrence of velocity slip. For the hydrophobic case, the gas completely fills the channel cross-section and experiences no velocity slip. In hydrophilic channels a stationary liquid fraction remains in the channel corners, allowing for the gas phase to achieve higher average velocity, and hence inducing velocity slip. The velocity profile within the gas slugs was also found to change as a function of contact angle, with hydrophilic channels inducing greater internal circulation, compared to greater channel wall contact in the case of hydrophobic channels. These effects play a role in heat from mass transfer from channels walls and highlight the value of numeral simulation in microfluidic design.

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 score0.225
Threshold uncertainty score0.607

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 head0.005
GPT teacher head0.231
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

Citations10
Published2010
Admission routes1
Has abstractyes

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