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Record W2023972987 · doi:10.1002/cjce.20301

Destabilisation of homogeneous bubbly flow in an annular gap bubble column

2010· article· en· W2023972987 on OpenAlexvenueno aff
Fahd M. Al‐Oufi, I.W. Cumming, Chris D. Rielly

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsBubbleSpargingPorosityMechanicsHomogeneousVoid (composites)Superficial velocityMaterials scienceLocal VoidConcentricFlow (mathematics)Two-phase flowComposite materialChemistryThermodynamicsGeometryPhysicsMathematics

Abstract

fetched live from OpenAlex

Abstract Experimental results are presented to show that there are very significant differences in the mean gas void fractions measured in an open tube and a annular gap bubble column, when operated at the same gas superficial velocity, using a porous sparger. Measurements were carried out in a vertical 0.102 m internal diameter column, with a range of concentric inner tubes to form an annular gap, giving diameter ratios from 0.25 to 0.69; gas superficial velocities in the range 0.014–0.200 m/s were investigated. The mean gas void fraction decreases with increasing ratio of the inner to outer diameter of the annular gap column and the transition to heterogeneous flow occurs at lower gas superficial velocities and lower void fractions. Two reasons are proposed and validated by experimental investigations: (1) the presence of the inner tube causes large bubbles to form near the sparger, which destabilise the homogeneous bubbly flow and reduce the mean void fraction; this was confirmed by deliberately injecting large bubbles into a homogeneous dispersion of smaller bubbles, and (2) the shape of the void fraction profiles changes with gap geometry and this affects the distribution parameter in the drift‐flux model. Both of these effects serve to reduce the mean gas void fraction in an annular gap bubble column compared to an open tube at the same gas superficial velocity.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.385

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 head0.168
Teacher spread0.163 · 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

Citations22
Published2010
Admission routes1
Has abstractyes

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