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Record W2024263948 · doi:10.1252/jcej.34.95

Models for Minimum Liquid Fluidization Velocity of Gas-Liquid Fluidized Beds.

2001· article· en· W2024263948 on OpenAlexaff
Dong‐Hyun Lee, Norman Epstein, John R. Grace

Bibliographic record

VenueJOURNAL OF CHEMICAL ENGINEERING OF JAPAN · 2001
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFluidizationBuoyancyDragThermodynamicsWork (physics)ChemistryViscosityMechanicsParticle (ecology)Fluidized bedPhysics

Abstract

fetched live from OpenAlex

The gas-perturbed liquid model of Zhang et al. (1995) is modified in an effort to improve its prediction of the minimum liquid velocity of fluidization, Ulmf, of a bed of solid particles in the presence of a low or moderate cocurrent flow of gas. Variants of the model are considered where the buoyancy term is based on the gas-liquid mixture, instead of the liquid alone, and with the frictional pressure gradient given by several alternative equations to the Ergun equation employed in the original gas-perturbed liquid model. All versions of the model provide similar dependence on such factors as gas velocity, particle diameter, particle density and liquid viscosity as those seen experimentally. The mixture buoyed equation with the drag based on an equation suggested by Foscolo et al. (1983) gives improved predictions over the original Zhang et al. (1995) model, but the best overall agreement is with buoyancy based on the liquid alone and the first term in the drag equation with the Carman (1937) constant of 180 instead of Ergun's 150. The predictions are sensitive to the minimum fluidization voidage, which is measured, assumed, or estimated. Further work is required to investigate minimum liquid fluidization velocities experimentally for particles of density closer to that of the liquid, and for high-viscosity liquids.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.207
Teacher spread0.196 · 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 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

Citations5
Published2001
Admission routes1
Has abstractyes

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