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Record W2152658985 · doi:10.1002/aic.11997

Investigation of nonuniformity in a liquid–solid fluidized bed with identical parallel channels

2009· article· en· W2152658985 on OpenAlexafffund
Long Fan, John R. Grace, Norman Epstein

Bibliographic record

VenueAIChE Journal · 2009
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaChinese Academy of SciencesSyncrude
KeywordsBaffleMechanicsFluentEulerian pathFlow (mathematics)Particle (ecology)Superficial velocityFluidized bedChannel (broadcasting)Work (physics)Computational fluid dynamicsFluidized bed combustionFlux (metallurgy)Materials scienceChemistrySimulationThermodynamicsPhysicsEngineeringGeologyElectrical engineeringLagrangian

Abstract

fetched live from OpenAlex

Abstract Previous work has demonstrated that multiphase flow through identical parallel channels and multiple cyclones can give rise to significant nonuniformity among the flow paths. This article presents results from a study where the distribution of voidage and flux through parallel channels in liquid–solid fluidized beds is investigated. Experiments and computational fluid dynamics simulations were performed with 1.2 mm glass beads fluidized by water where a cross baffle divided a 191 mm diameter column into four identical parallel channels. Voidages were measured by optical fiber probes. Simulations from a three‐dimensional unsteady‐state Eulerian–Eulerian model based on FLUENT software showed good agreement with the experimental results. Despite the symmetrical geometry of the system, the average voidage and particle velocities in one channel differed somewhat from those in the others. Increasing the superficial liquid velocity could increase voidage greatly and affect the degree of nonuniformity in the four channels. © 2009 American Institute of Chemical Engineers AIChE J, 2009

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 score0.041
Threshold uncertainty score0.486

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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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

Citations12
Published2009
Admission routes2
Has abstractyes

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