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Record W1966026205 · doi:10.1115/1.4026282

Implementation of Two-Fluid Model for Dilute Gas-Solid Flow in Pipes With Rough Walls

2013· article· en· W1966026205 on OpenAlexafffund
Ashraf Uz Zaman, Donald J. Bergstrom

Bibliographic record

VenueJournal of Fluids Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTurbulenceMechanicsTurbulence kinetic energyReynolds stressTurbulence modelingMaterials scienceFlow (mathematics)Particle (ecology)Two-phase flowSurface finishTwo-fluid modelReynolds numberThermodynamicsPhysicsGeologyComposite material

Abstract

fetched live from OpenAlex

A numerical study was carried out to investigate the performance of a two-layer model for predicting turbulent gas-particle flows in rough pipes. An Eulerian–Eulerian two-fluid formulation was used to model both the gas and solid phases for turbulent gas-particle flow in a vertical tube. The stresses developed in the particle phase were calculated using the kinetic theory of granular flows while the gas-phase stresses were described using an eddy viscosity model. The two-fluid model typically uses a two-equation k-ɛ model to describe the gas phase turbulence, which includes the suppression and enhancement effects due to the presence of particles. For comparison, a two-layer model was also implemented since it has the capability to include surface roughness. The current study examines the predictions of the two-layer model for both clear gas and gas-solid flows in comparison to the results of a conventional low Reynolds number model. The paper specifically documents the effects of surface roughness on the turbulence kinetic energy and granular temperature for gas-particle flow in both smooth and rough pipes.

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 score0.264
Threshold uncertainty score0.765

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 communication0.0000.001
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 head0.009
GPT teacher head0.251
Teacher spread0.242 · 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

Citations7
Published2013
Admission routes2
Has abstractyes

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