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Record W1974200457 · doi:10.1002/cjce.21716

Particle dispersion and fluctuations in viscous liquid–solid fluidised Beds

2012· article· en· W1974200457 on OpenAlexfundvenueaboutno aff
In Soo Han, Hyun Oh Lim, Kee Tae Kim, Yong Kang, Ki Won Jun

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
FundersKorea Institute of Energy Technology Evaluation and PlanningUniversity of British Columbia
KeywordsPorosityDispersion (optics)Materials scienceHomogeneousDrop (telecommunication)Viscous liquidParticle sizeMaximaThermodynamicsParticle (ecology)MechanicsChemistryComposite materialPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract Particle dispersion and fluctuations were investigated in a liquid–solid fluidised bed with viscous liquid media by employing the relaxation method based on the stochastic model. The bed expansion or contraction during the transient state was analysed by means of the histogram of pressure drop variation with elapsed time. The relaxation behaviour of the liquid–solid fluidised bed appeared to be heterogeneous followed by homogeneous expansion or contraction. Effects of liquid velocity ( U L ), particle size ( d P ), liquid viscosity ( µ L ) and liquid holdup or bed porosity ( ε L ) on the fluctuating frequency ( F ) and dispersion coefficient ( D P ) of fluidised solid particles were examined. The values of F and D P increased with an increase in d P but decreased with µ L and exhibited local maxima with variations of U L and ε L . The flow pattern of solid particles could be changed from uniform or pseudo‐homogeneous to turbulent random behaviour with an increase in the liquid velocity and liquid holdup or bed porosity. © 2012 Canadian Society for Chemical Engineering

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 score0.110
Threshold uncertainty score0.317

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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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

Citations4
Published2012
Admission routes3
Has abstractyes

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