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Record W2023685555 · doi:10.1002/fld.1729

Numerical model for the prediction of dilute, three‐dimensional, turbulent fluid–particle flows, using a Lagrangian approach for particle tracking and a CVFEM for the carrier phase

2008· article· en· W2023685555 on OpenAlexafffund
Luís Adriano Freitas Oliveira, V.A.F. Costa, B. R. Baliga

Bibliographic record

VenueInternational Journal for Numerical Methods in Fluids · 2008
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTurbulenceMechanicsEulerian pathParticle-laden flowsLagrangian particle trackingPhysicsParticle (ecology)CompressibilityTwo-fluid modelTwo-phase flowComputational fluid dynamicsClassical mechanicsFluid dynamicsVolume of fluid methodFlow (mathematics)LagrangianReynolds numberGeology

Abstract

fetched live from OpenAlex

Abstract A numerical model for dilute, three‐dimensional, turbulent, incompressible fluid–solid particle flows and its application to a demonstration problem are presented. An Eulerian description is used to model the flow of the fluid (carrier) phase, and the governing equations are solved using a control‐volume finite element method (CVFEM). The motion of the solid (particulate) phase is simulated using a Lagrangian approach. An efficient algorithm is proposed for locating the particles in the finite element mesh. In the demonstration problem, which involves a particle‐laden axisymmetric jet, a modified k –ε turbulence model is used to characterize the velocity and length scales of the turbulent flow of the fluid phase. The effect of turbulence on the particle trajectories is accounted for through a stochastic model. The effect of the particles on the fluid time–mean velocity and turbulence (two‐way coupling) is also addressed. Comparisons between predictions and available experimental data for the demonstration problem are presented. Satisfactory agreement is obtained. Copyright © 2008 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.365
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.108
GPT teacher head0.388
Teacher spread0.280 · 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
GenreMethods

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

Citations3
Published2008
Admission routes2
Has abstractyes

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