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Record W1919353046 · doi:10.1002/cjce.22282

Effect of orifice size and bond number on bubble formation characteristics: A CFD study

2015· article· en· W1919353046 on OpenAlexvenueno aff
Md. Tariqul Islam, P. Ganesan, J.N. Sahu, Shanti C. Sandaran

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsBubbleBody orificeMechanicsLiquid bubbleVolume of fluid methodComputational fluid dynamicsMaterials scienceVolume (thermodynamics)PinchChemistryPhysicsThermodynamicsMechanical engineeringFlow (mathematics)Engineering

Abstract

fetched live from OpenAlex

The volume of fluid with the continuum surface force (VOF‐CSF) model has been used in the present numerical study to investigate bubble formation and shapes in a bubble column. The effects of orifice sizes ranging from 0.5–1.5 mm on the bubble formation stages (i.e., expansion, elongation, and pinch‐off), bubble contact angle, departure diameter, time, and shape of bubble are investigated under a constant inlet velocity (0.2 m/s) boundary condition (BC). It is found that the initial formation of a bubble is dependent on the orifice diameter. Consequently, the formation of the bubble's hemispherical shape at the orifice is faster for a smaller orifice diameter than for a larger orifice diameter which forms a bigger bubble. A leading bubble requires a longer time to detach itself from an orifice in comparison to the next bubble (the second bubble), but interestingly the third bubble detaches much faster than the second. This model was also used to investigate the effect of the Bond number (Bo σ ). An increase of the Bond number (Bo σ ) from 0.047 to 0.16 speeds up the bubble pinch‐off, but a further increase in the Bo σ (e.g. 0.17–0.47) slows it down. Based on the simulated cases used in this study, the findings demonstrate the capacity and accuracy of the CFD method in predicting bubble formation characteristics. The findings may be useful in the design of spargers for bubble column reactors.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.314

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 head0.186
Teacher spread0.182 · 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

Citations22
Published2015
Admission routes1
Has abstractyes

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