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Record W2954576194 · doi:10.1007/s10035-019-0922-6

Rheometry of dense granular collapse on inclined planes

2019· article· en· W2954576194 on OpenAlexafffund
Olalekan Rufai, Yee‐Chung Jin, Yih‐Chin Tai

Bibliographic record

VenueGranular Matter · 2019
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGranular materialMechanicsRheologyMaterials scienceRheometryDilatantShear (geology)Inclined planeFlow (mathematics)Deformation (meteorology)PhysicsComposite material

Abstract

fetched live from OpenAlex

The continuum deformation of gravity-driven dense granular materials on steep inclined planes exhibit similar flow phenomenon to natural and industrial occurrences. This kind of granular flow dynamics corresponds to unsteady flows which currently lack any theoretical formulations for its kinematic properties. The implementation of the µ ( I ) rheology model and the moving particle semi-implicit mesh-free method as a coupled set is used to predict quantitatively the flow properties and phenomena of the granular materials on inclined planes. During deformation of the granular materials, transition of flow regime shows that the velocity profile is maintained at the dense region but fluctuates when tending to the dilute region due to limited interaction between granular materials. The granular materials shear stress is found to attain a yield point before reducing as the shear rate increases exhibiting a transient behaviour. We also compared the velocity profile, surface profile and wave front of the flowing granular materials with physical studies on a two-dimensional configuration giving acceptable qualitative agreements.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.191
Teacher spread0.185 · 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

Citations11
Published2019
Admission routes2
Has abstractno

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