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Record W2562968272 · doi:10.1115/1.4035562

Investigation of Bubble Frequency for Slug Flow Regime in a Uniformly Heated Horizontal Microchannel

2016· article· en· W2562968272 on OpenAlexaff
Amen Younes, Ibrahim Hassan, Lyes Kadem

Bibliographic record

VenueJournal of Heat Transfer · 2016
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsBubbleSlug flowMechanicsCoalescence (physics)MicrochannelBoilingMass fluxThermodynamicsFlow (mathematics)Materials scienceHeat fluxTwo-phase flowPhysicsHeat transfer

Abstract

fetched live from OpenAlex

Slug flow is an essential flow pattern observed in microchannels where its transition boundaries in microchannels are characterized by two complex hydrodynamic phenomena, the bubble confinement and the bubble coalescence. Slug flow may be classified in terms of bubble size into two major zones: isolated bubble zone and coalescence bubble zone. In this paper, a semi-analytical model is developed for predicting the main characteristics of isolated bubble zone for flow boiling in a horizontal microchannel. The influences of surface tension, shear, and inertial forces have been taken into account. The model is developed on the basis of drift flux model, and a fully developed slug unit is chosen as a control volume for deriving the equations of motion. The effects of main operating conditions, mass and heat fluxes, on bubble length and bubble frequency have been investigated. The boundaries of slug flow regime have been identified based on the most proper diabatic flow pattern maps available in the literature for the chosen database. The model has been validated using the database available in the literature for flow boiling of R134a and R245fa in 0.509 mm and 3.0 mm inner diameter horizontal mini-tubes, respectively, and over wide range of mass fluxes (300≤G≤1000 kg/m2 s). This study has shown that the mass flux has a significant effect on the slug length and the bubble frequency. The model gave a good agreement with the experimental data of bubble length and bubble frequency with a mean absolute error (MAE) of 18.0% and 27.34%, respectively.

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 score0.069
Threshold uncertainty score0.449

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 head0.017
GPT teacher head0.210
Teacher spread0.192 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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