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Record W2032844494 · doi:10.1115/pvp2014-28460

Void Fraction Effect on Added Mass in Bubbly Flow

2014· article· en· W2032844494 on OpenAlexaff
Cédric Béguin, Etienne Pelletier, Stéphane Étienne

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMechanicsVoid (composites)Euler's formulaSpherical harmonicsTwo-phase flowEuler equationsPorosityBubbleMultiphase flowMass fractionFlow (mathematics)Fraction (chemistry)MathematicsStatistical physicsMaterials scienceClassical mechanicsThermodynamicsPhysicsMathematical analysisChemistryChromatography

Abstract

fetched live from OpenAlex

This paper proposes a relation for the added mass coefficient of spherical bubbles depending on void fraction based on results obtained by a semi-analytical method. This information is essential to completely characterize finely dispersed bubbly flows, where small spherical gas bubbles are present in a continuous liquid phase. Most of the closure relations for Euler-Euler or Euler-Lagrange models are obtained from experiments involving single bubbles. Their applicability to systems with high void fraction is therefore questionable. This paper uses solid harmonics to solve 3D potential flow around bubbles. Several configurations were calculated for different numbers of particles and spatial arrangements. Our results are compared with previous studies. Depending on the model proposed by previous authors, added mass forces could increase or decrease with the void fraction. This paper solves these discrepancies. The main purpose of this work is to develop simple formulas fitting our semi-analytical results. These simple formulas are suitable for further use, particularly as added mass models for multiphase flow averaged equations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.310
Threshold uncertainty score0.999

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)0.0050.002

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 head0.006
GPT teacher head0.245
Teacher spread0.239 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2014
Admission routes1
Has abstractyes

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