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Record W2035985495 · doi:10.1002/cjce.5450810325

Bubble Breakup and Coalescence in a Plunging Liquid Jet Bubble Column

2003· article· en· W2035985495 on OpenAlexvenueno aff
B. W. Atkinson, Graeme J. Jameson, Anh V. Nguyen, Geoffrey M. Evans, P. Machniewski

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsBubbleCoalescence (physics)MechanicsBreakupWeber numberDissipationMaterials sciencePhysicsThermodynamicsReynolds numberTurbulence

Abstract

fetched live from OpenAlex

Abstract In this study the bubble size distribution and energy dissipation rate are measured in a plunging liquid jet bubble column. The maximum stable bubble diameter in the mixing zone was successfully predicted based on Weber number analysis. A Pareto analysis indicated that the coalescence of bubbles principally occurred as a binary process. The bubble number flux was modeled based on the bubble number concentration, collision frequency of two bubbles of the same diameter, and the probability of coalescence taking place. In general, the predictions were in good agreement with the measurements for both the mixing and pipe flow zones.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.491

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 head0.003
GPT teacher head0.153
Teacher spread0.150 · 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 designSimulation or modeling
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

Citations21
Published2003
Admission routes1
Has abstractyes

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