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Record W2001542744 · doi:10.1002/cjce.21803

Modelling of penetration of horizontal supersonic nozzles in high temperature fluidised beds

2013· article· en· W2001542744 on OpenAlexafffundvenueabout
Feng Li, Cédric Briens, Franco Berruti, Jennifer McMillan

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsSyncrude (Canada)Western University
FundersSyncrudeKlingenstein Third Generation Foundation
KeywordsSupersonic speedNozzlePenetration (warfare)FluidizationMechanicsTuyereMaterials scienceJet (fluid)Kinetic energyChoked flowFluidized bedThermodynamicsPhysicsEngineeringClassical mechanicsMetallurgy

Abstract

fetched live from OpenAlex

Abstract Supersonic nozzles have been applied in various jet‐induced fluidised bed attrition processes such as jet milling and Fluid Coking. In jet‐induced particle attrition, the penetration length into the bed of the jet issuing from the supersonic nozzle is a critical property that affects the attrition mechanisms. A numerical model was developed to predict the penetration length of jets issuing from a horizontal supersonic nozzle in high temperature fluidised beds, based on an Eulerian–Eulerian multiphase model and Granular kinetic theory. The predicted jet penetration lengths are in very good agreement with the experimental data and the predictions of Li's empirical correlation [Li, “Penetration of High Velocity Horizontal Gas Jets Into a Fluidized Bed at High Temperature”, in Fluidization XIII , S. D. Kim, Y. Kang, J. K. Lee, Y. C. Seo, Eds., Gyeong‐ju, Korea 2010 ; Engineering Conferences International , Gyeong‐ju, Korea 2010 , pp. 893–900.]. The simulation results have also demonstrated that the fluidisation velocity and bed temperature have little influence on jet penetration length. © 2013 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 score0.029
Threshold uncertainty score0.406

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 head0.149
Teacher spread0.143 · 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

Citations6
Published2013
Admission routes4
Has abstractyes

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