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Record W4281782136 · doi:10.3390/en15113967

Measuring Interparticle Friction of Granules for Micromechanical Modeling

2022· article· en· W4281782136 on OpenAlexafffund
Yuan Li, Dave Chan, Alireza Nouri

Bibliographic record

VenueEnergies · 2022
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsMaterials scienceShear (geology)Direct shear testGranular materialBall (mathematics)MechanicsDynamical frictionComposite materialDisplacement (psychology)GeometryPhysicsMathematics

Abstract

fetched live from OpenAlex

The aim of this paper is to develop an experimental procedure to measure contact friction between granular particles. The contact friction is a micro-property needed in the micromechanical modeling of a granular medium. The proposed method can measure the interparticle friction of idealized spherical particles using the conventional direct shear apparatus in soil testing. In preparation for the test, the test specimen is made of four steel balls embedded halfway in a sulfaset paste plate positioned in a statically determinant configuration to provide point contacts among the steel balls. The upper half of the shear box contains one steel ball, which is supported by three steel balls in the lower shear box, ensuring contact points at all times during the test. Shear force and shear displacement are measured under a specific normal force during the test. An analytical equation is developed based on the geometrical configuration of the balls to calculate the interparticle friction angle. The test is shown to be repeatable, and the calculated interparticle friction angle agrees well with experimental measurements with a high degree of accuracy and consistency.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.270

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 head0.028
GPT teacher head0.197
Teacher spread0.169 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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