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Record W1925038983 · doi:10.1002/cjce.22295

Numerical analysis of the interphase forces in bubble columns using euler‐euler modelling framework

2015· article· en· W1925038983 on OpenAlexvenueno aff
Renato Soccol, Ana Carolina Galliani Piscke, Dirceu Noriler, Ivan Carlos Georg, Henry França Meier

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersFinanciadora de Estudos e Projetos
KeywordsDragLift (data mining)Eulerian pathMechanicsBubbleTurbulenceRepresentation (politics)Euler's formulaDrag coefficientContext (archaeology)Euler equationsClassical mechanicsPhysicsComputer scienceMathematicsMathematical analysisTheoretical physicsThermodynamicsGeology

Abstract

fetched live from OpenAlex

In the context of modelling the gas‐liquid multiphase flow in bubble columns, interface forces, such as drag, lift, and virtual mass forces, are of fundamental importance with regard to the accurate representation of physical phenomena. The approach used in this study was the Eulerian‐Eulerian modelling of turbulence in the continuous phase using the standard k‐ϵ model. Different models for the drag coefficient (C D ) found in the literature were tested to obtain the best representation of the experimental data available in the literature. The relevance of the lift and virtual mass forces was also studied. The results show that the inclusion of lift contributes significantly to improving the representation of the experimental data when used in conjunction with the drag force. The inclusion of the virtual mass force did not prove to be relevant.

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 score0.113
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.209
Teacher spread0.193 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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