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Record W4212847342 · doi:10.1017/jfm.2022.44

A generalized model for dense axisymmetric grains flow with orientational diffusion

2022· article· en· W4212847342 on OpenAlexafffund
Meitham Amereh, Ben Nadler

Bibliographic record

VenueJournal of Fluid Mechanics · 2022
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlow (mathematics)Rotational symmetryOrientation (vector space)DiffusionMechanicsFlux (metallurgy)Flow propertiesConvectionField (mathematics)PhysicsBoundary (topology)Boundary value problemMaterials scienceClassical mechanicsCondensed matter physicsGeometryThermodynamics

Abstract

fetched live from OpenAlex

The flow of non-spherical grains is strongly affected by the orientation of the grains, as observed in experiments and simulations. An existing model that predicts the orientation of grains as a function of the flow field and shape of the grains, is limited to homogenous orientational fields where the interaction of the grains with their surroundings is negligible. To address this limitation, we generalized the model to account for inhomogeneous orientational fields in the form of spatial non-convective orientational flux. The orientational flux accounts for the interactions of grains with their surroundings and the boundaries. The proposed model is used to study the influence of orientational diffusion and boundary conditions on the flow of dense granular material down an incline. It is shown that the boundary conditions can play a significant role in the orientation of grains in such flows.

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)0.000
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.213
Teacher spread0.199 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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