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Record W4410992337 · doi:10.1016/j.jcp.2025.114120

A polydisperse Gaussian-moment model for dilute turbulent multiphase flows

2025· article· en· W4410992337 on OpenAlexafffund
Benoit J. Allard, Lucian Ivan, James G. McDonald

Bibliographic record

VenueJournal of Computational Physics · 2025
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsCanadian Nuclear LaboratoriesUniversity of Ottawa
FundersOffice of ScienceAlliance de recherche numérique du CanadaCanadian Nuclear LaboratoriesAtomic Energy of Canada Limited
KeywordsTurbulenceMoment (physics)Statistical physicsSecond moment of areaGaussianMechanicsPhysicsGeologyMathematicsApplied mathematicsClassical mechanicsThermodynamics

Abstract

fetched live from OpenAlex

The accurate modelling of polydisperse multiphase flows in which a discrete particle phase is suspended in a background fluid remains a considerable challenge. Reliable numerical predictions are especially difficult for turbulent background flows in which a turbulence model is used to account for unresolved turbulent scales. Currently, Lagrangian descriptions for the particle phase are the most common. However, the numerical cost associated with such an approach can be prohibitive. This is especially true when particles are differentiated by numerous properties, thus greatly increasing the number of particles needed for a reliable prediction. Even when sufficient particles are used, the exact effect of unresolved turbulent effects on the particle phase remains difficult to describe. Eulerian descriptions are often more affordable, but traditionally describe only a few low-order statistics of the particle phase and present known mathematical artifacts. More recently, the polydisperse Gaussian moment method (PGM) has been proposed as an Eulerian model for polydisperse flows that includes second-order statistics between all particle properties. This paper demonstrates a simple method by which PGM models can be coupled to an unresolved background turbulent flow field. This treatment leads to an algebraic source of local particle-velocity variance that is caused by turbulent fluctuations. The new model is used to recreate classical experimental dispersion results for particles in a turbulent wind tunnel . Though good agreement is found for monodisperse flows, it is found that the turbulent PGM results spuriously overpredict the dispersion of larger particles. The source of this discrepancy is identified as a limitation of all moment methods with only second-order statistics of multiphase flows exhibiting additional distinguishing properties. In actual flows, turbulence should lead to more dispersion of smaller particles. However, proper statistical treatments of this effect would require a third-order moment relating velocity variance to particle size. This moment is assumed to be zero in the PGM. Thus, this effect cannot be properly captured by PGM methods, regardless of the turbulence model used.

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: none
Teacher disagreement score0.672
Threshold uncertainty score0.518

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 head0.014
GPT teacher head0.274
Teacher spread0.259 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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