MétaCan
Menu
Back to cohort
Record W2034217768 · doi:10.1002/cjce.22196

Comparative Analysis of Coalescence and Breakage Kernels in Vertical Gas‐Liquid Flow

2015· article· en· W2034217768 on OpenAlexvenueno aff
Lilunnahar Deju, Sherman C.P. Cheung, Guan Heng Yeoh, Fengsheng Qi, Jiyuan Tu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsBreakageBubbleCoalescence (physics)MechanicsTurbulenceBreak-UpFlow (mathematics)Two-phase flowMaterials sciencePhysics

Abstract

fetched live from OpenAlex

The evolution of bubble size distribution is an important consideration in vertical gas‐liquid flow, especially in determining the appropriate mass, momentum, and heat transfer between two phases. In order to adequately capture the distribution and to account for its effect on the local hydrodynamics, which generally represents the dominant flow characteristic in such a practical system, a numerical assessment has been performed to understand the six widely adopted different bubble coalescence and bubble breakage kernels. Three different breakage kernels have been selected where each kernel considers a different shape of the daughter size distribution of the bubbles, such as the U‐shape, bell‐shape, and M‐shape. These are combined with different coalescence kernels. The bubble size distribution, void fraction, interfacial area concentration, and gas velocity profiles are compared against the experimental data. Numerical results reveal that the effect on the two‐phase flow structure is mainly due to the application of the different breakage kernels. Moreover, the predicted results also show that the bell‐shape daughter size distribution favours equal breakage of bubbles, which could lead to the over‐prediction of large bubbles. A more sophisticated model for handling bubble induced turbulence should nonetheless be applied in future investigations of vertical gas‐liquid flow.

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 score0.046
Threshold uncertainty score0.317

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 head0.012
GPT teacher head0.201
Teacher spread0.190 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicFluid Dynamics and MixingFrench-language works237,207