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Record W4389738479 · doi:10.1002/aic.18328

The bubble size and rise velocity of bubble‐induced three‐phase inverse fluidized bed (BIFB)

2023· article· en· W4389738479 on OpenAlexaff
Keying Ma, Zeneng Sun, Yuanyuan Shao, Jesse Zhu

Bibliographic record

VenueAIChE Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsWestern University
Fundersnot available
KeywordsBubbleMechanicsCoalescence (physics)BreakupFluidized bedParticle sizeMaterials sciencePhysicsChemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract The bubble size and rise velocity were investigated in detail using a new telecentric photographic probe in a pilot‐scale bubble‐induced three‐phase inverse fluidized bed (BIFB). By the influence of the bubble flow behavior, the particle bed goes through four different flow regimes with increasing gas velocity in BIFB. An obvious normal bubble size distribution caused by bubble coalescence and breakup was observed. A parameter of “size span” was proposed to describe the distribution width of bubble size. The average bubble size increases with the axial distance from the column bottom and with the gas velocity until reaching a relatively stable size. The radial distributions of the average bubble size are almost uniform but experience a decreasing trend near the column wall. The bubble rise velocity increases with the bubble size and the gas velocity. An empirical equation was established to predict successfully the bubble rise velocity in BIFB.

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)0.000
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 score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.015
GPT teacher head0.242
Teacher spread0.227 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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