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Record W2035012399 · doi:10.1002/cjce.5450810368

Effect of Gas Density on the Hydrodynamics of Bubble Columns and Three‐Phase Fluidized Beds

2003· article· en· W2035012399 on OpenAlexafffundvenue
Arturo Macchi, Hsiaotao T. Bi, John R. Grace, Craig A. McKnight, Larry P. Hackman

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsSyncrude (Canada)University of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaSyncrude
KeywordsBubbleDistributorHeliumCarbon dioxideMaterials scienceVolume (thermodynamics)Borosilicate glassGas bubbleChemistryGas phaseVolumetric flow rateAqueous solutionAnalytical Chemistry (journal)ThermodynamicsMechanicsChromatographyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Experiments were performed at ambient temperature and pressure in a 127 mm inner diameter column with a 55% wt. aqueous glycerol solution, 6‐mm spherical borosilicate beads and four gases — helium, air, carbon dioxide and sulphur hexafluoride — giving a 35‐fold gas density range. The dispersed bubble flow regime was sustained to higher gas velocities and gas holdups for denser gases. This finding appears to be due to the reduction of the maximum stable bubble size (i.e. enhanced bubble break‐up), rather than to formation of smaller bubbles at the distributor with increasing gas density. The effect of gas density was significant both with and without the particles present, with gas holdup increasing, bed voidage increasing and liquid holdup decreasing with increasing gas density. The holdup correlations of Han et al. (1990) have been modified to incorporate the effect of gas density.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

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 communication0.0010.000
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 head0.171
Teacher spread0.168 · 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 designBench or experimental
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

Citations16
Published2003
Admission routes3
Has abstractyes

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