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Record W2006275796 · doi:10.1002/cjce.5450810310

Mixing in Bubble Column Reactors: Role of Unsteady Flow Structures

2003· article· en· W2006275796 on OpenAlexvenueno aff
Vivek V. Buwa, Vivek V. Ranade

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsMixing (physics)MechanicsBubbleComputational fluid dynamicsFlow (mathematics)Work (physics)Transient (computer programming)Bubble column reactorMaterials scienceThermodynamicsPhysicsGas bubbleComputer science

Abstract

fetched live from OpenAlex

Abstract Gas‐liquid flows in bubble column reactors are inherently unsteady. In the present work, we have characterized dynamics of gas‐liquid flow in rectangular bubble columns and studied the role of unsteady flow structures in liquid phase mixing using experiments and CFD simulations. The need for considering the unsteady nature of gas‐liquid flows was demonstrated by using mixing simulations carried out with transient and time‐averaged flow. A new methodology of using a computationally amenable 'multiple snapshots' approach, which can adequately account for dynamics of underlying flow, is proposed. CFD simulations were carried out to study the influence of superficial gas velocity and height‐to‐width ratio on mixing time. The predictions were compared with mixing time measured using conductivity probes. The present work gives useful insights into the mixing process in inherently unsteady flows.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.153
Teacher spread0.150 · 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 source (direct Gemma or distilled Codex), 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

Citations60
Published2003
Admission routes1
Has abstractyes

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