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Record W2122451080 · doi:10.1002/cjce.21812

Turbulence effects on the granular model of particle motion in a boundary layer flow

2013· article· en· W2122451080 on OpenAlexvenueno aff
Maziar Dehghan, Hassan Basirat Tabrizi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsnot available
Fundersnot available
KeywordsTurbulenceMechanicsParticle (ecology)K-epsilon turbulence modelBoundary layerParticulatesK-omega turbulence modelTurbulence kinetic energyFlow (mathematics)PhysicsParticle-laden flowsTurbulence modelingStokes numberTwo-phase flowParticle velocityClassical mechanicsMaterials scienceGeologyChemistryReynolds number

Abstract

fetched live from OpenAlex

Abstract Prediction of particle velocity in a dilute turbulent gas–particle flow nearby a flat solid boundary is investigated numerically using a fully Eulerian two‐fluid model. The particulate phase model is based on the kinetic theory of granular flow. The turbulence in both phases is modelled. Inter‐particle and particle‐wall inelastic collisions are considered. Effects of presence/absence of particulate phase turbulence modelling on velocity of particles are investigated. Results indicate that turbulence modelling is necessary for particulate phase, but conventional models should be modified to consider particulate flow nature. Effects of free stream velocity and particle diameter on turbulence modelling results have been discussed.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.173
Teacher spread0.165 · 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 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

Citations14
Published2013
Admission routes1
Has abstractyes

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