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Record W2155988116 · doi:10.1260/1757-482x.2.1.59

Simulation of the Steam-Bitumen Jet in the Fluidized Bed of Coke Particles Using the Eulerian-Lagrangian Splitting Method

2010· article· en· W2155988116 on OpenAlexafffund
P.F. Nowak, Konstantin Pougatch, M. Salcudean

Bibliographic record

VenueThe Journal of Computational Multiphase Flows · 2010
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCokeAgglomerateMaterials scienceJet (fluid)NozzleMechanicsAsphaltFluidized bedEconomies of agglomerationBreakupMultiphase flowThermodynamicsComposite materialChemical engineeringMetallurgyPhysicsEngineering

Abstract

fetched live from OpenAlex

Simulation of the steam-bitumen jet injected into the steam-coke fluidized bed is presented. The computational method is based on the Eulerian-Lagrangian splitting. The Eulerian two-fluid model is used for the steam and the coke phases; the latter is composed of dry and wet coke particles. The Lagrangian model is applied to the bitumen droplets and the primary bitumen-coke agglomerates, generated by the random collisions of the droplets with the coke particles. A simple primary agglomeration-breakup model based on the experimental evidence is used. The gravity and the fluidizing cross-flow are neglected. Consequently, the computational problem is led to the axisymmetric form. Realistic results were obtained for the steam parameters at the end of the nozzle, the jet half-angle, and the location of the areas with the predominant agglomerations and breakups. The composition and stability of the agglomerates, and the mass transfer between the agglomerates and the coke phase are investigated.

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)0.003
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 score0.278
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
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 head0.021
GPT teacher head0.291
Teacher spread0.270 · 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

Citations1
Published2010
Admission routes2
Has abstractyes

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