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Record W2154298384 · doi:10.1002/aic.12171

CFD simulations of hydrodynamic/thermal coupling phenomena in a bubble column with internals

2010· article· en· W2154298384 on OpenAlex
C. Laborde-Boutet, Faı̈çal Larachi, Nicolas Dromard, Olivier Delsart, Pierre-Emmanuel Béliard, Daniel Schweich

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAIChE Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsComputational fluid dynamicsMechanicsBubbleCoupling (piping)Column (typography)ThermalMaterials scienceThermodynamicsPhysicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract CFD simulations have been carried out in a full three‐dimensional, unsteady, Eulerian framework to simulate hydrodynamic/thermal coupling in a bubble column with internals. A first part of the study, dedicated to the hydrodynamic/thermal coupling in liquid single‐phase flows, showed that assuming constant wall temperature on the internals constitutes a reasonable approximation in lieu of comprehensive simulations encompassing shell flow and coolant flow together. A second part dealing with the hydrodynamics of gas–liquid flows in a bubble column with internals showed that a RNG k–ε turbulence model formulation accounting for gas‐induced turbulence was a relevant choice. The last part used these conclusions to build a hydrodynamic/thermal coupling model of a gas–liquid flow in a bubble column with internals. With a per‐phase RNG k–ε turbulence model and assuming constant wall temperature, it was possible to simulate heat transfer phenomena consistent with experimentally measured heat transfer coefficients. © 2010 American Institute of Chemical Engineers AIChE J, 2010

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.

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 score0.139
Threshold uncertainty score0.355

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 integrity0.0000.001
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 head0.003
GPT teacher head0.194
Teacher spread0.191 · 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