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Record W4310262013 · doi:10.3389/fphy.2022.963495

The modeling of free-fall arch formation in granular flow through an aperture

2022· article· en· W4310262013 on OpenAlexaff
Yao Tang, Dave Chan, David Z. Zhu

Bibliographic record

VenueFrontiers in Physics · 2022
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of Alberta
FundersHigher Education Discipline Innovation ProjectZhejiang UniversityNingbo UniversityNational Natural Science Foundation of China
KeywordsArchMechanicsGranular materialFlow (mathematics)Aperture (computer memory)Particle (ecology)Geotechnical engineeringMaterials scienceGeologyPhysicsEngineeringStructural engineering

Abstract

fetched live from OpenAlex

In this study, to predict the formation of the free-fall arch via granular flow through an aperture, an analytical model has been developed based on the particle-scale force equilibrium. This model calculates the size and location of the meta-stable arch and can be extended to predict the granular flow rate. According to the developed analytical model, the formation of a free-fall arch is independent of granular height and stress state above the arch, where only granular particle size, aperture size, and granular friction influence the development of the arch. Besides, this proposed model can predict the formation of the meta-stable arch without empirical parameters. In comparison with experimental results, the predicted granular flow rate based on the model exhibits high accuracy for uniform-sized granular flow. According to numerical simulations, the free-fall arch appears above the aperture; however, the particle velocity at the arch is low and can be considered negligible. Gravity will cause the granular particles under the arch to fall freely. This mathematical model offers an efficient method to predict the formation of the free-fall arch and calculate the granular flow rate through an aperture.

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 score0.828
Threshold uncertainty score0.392

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 head0.011
GPT teacher head0.201
Teacher spread0.190 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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