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Record W2089222715 · doi:10.1002/cjce.21839

Computational fluid dynamics validation and comparison analysis of scale‐up relationships of spouted beds

2013· article· en· W2089222715 on OpenAlexvenueno aff
Wei Du, Jian Xu, Weisheng Wei, Xiaojun Bao

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
Fundersnot available
KeywordsDimensionless quantityAnnulus (botany)MechanicsComputational fluid dynamicsScalingBody orificeDynamic similarityFluid dynamicsFlow (mathematics)Scale (ratio)Materials scienceReynolds numberMathematicsPhysicsGeometryTurbulenceEngineering

Abstract

fetched live from OpenAlex

Abstract A computational fluid dynamics (CFD) approach was attempted to investigate the hydrodynamic similarity of spouted beds of different sizes. The dimensionless scaling groups were derived by analysing the solid stress tensors with the kinetic theory of granular flow (KTGF). Results show that U ms , the minimum spouting velocity and the dimensionless spout diameter ( D s / D c ), become smaller with the increasing column diameter ( D c ) as the fluid flares out to the annulus immediately above the orifice, and the portion of fluid in the annulus region increases with the increasing column diameter. Importantly, we observed distinctive dissimilarity in the voidage profiles and dimensionless particle velocities in the upper part of spouted beds, especially for larger columns. New correlations on spout diameter and U ms were derived based on the scaling‐up analysis by regression of experimental data appeared in literatures for both small and large columns, which can greatly improve the predictions for spouted beds as compared with those previous equations, especially for large columns with deviations less than 15% for most of data.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.328

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 head0.010
GPT teacher head0.190
Teacher spread0.181 · 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 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

Citations8
Published2013
Admission routes1
Has abstractyes

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