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Record W2056209621 · doi:10.1002/cjce.21869

Evaluation on the effect of conical geometry on flow behaviours in spouted beds

2013· article· en· W2056209621 on OpenAlexvenueno aff
Xuejiao Liu, Wenqi Zhong, Yingjuan Shao, Bing Ren, Baosheng Jin

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
Fundersnot available
KeywordsConical surfaceMechanicsLigand cone angleAnnulus (botany)GeometryFlow (mathematics)Materials scienceParticle (ecology)Computational fluid dynamicsFountainCFD-DEMPhysicsGeologyMathematicsComposite material

Abstract

fetched live from OpenAlex

Abstract Evaluation on the effect of conical section geometry on the gas–solid flow behaviours in conical–cylindrical spouted beds were carried out by three‐dimensional computational fluid dynamics (CFD) coupling with two‐fluid model (TFM). The simulations were implemented in seven columns with their conical angles ranging from 30° to 180°. The solid flow pattern, particle velocity, voidage, fountain height and the spout diameter were investigated. It was found that unstable spouting would occur when the conical angle is less than 30°. In addition, the conical angle of 105° might be a borderline value for gas–solid flow behaviours in the spout. In this region, increasing conical angle leads to a decrease in fountain height, particle velocity and voidage and an increase in the spout diameter when the conical angle is within 105°, while reverse trends could be found when the conical angle is beyond 105°. The smallest fountain height, particle velocity and voidage and largest spout diameter could be found when the angle is 105°. In spite of these, the particle velocity in the annulus continually increases with increasing conical angle.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.008
GPT teacher head0.194
Teacher spread0.187 · 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 designObservational
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

Citations20
Published2013
Admission routes1
Has abstractyes

Explore more

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