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Numerical Investigation of Statistical Properties of Microslips in Silos

2003· article· en· W2041581226 on OpenAlex
Oleg Vinogradov, Yuri Leonenko

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A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Engineering Mechanics · 2003
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSiloMechanicsParticle numberPlanarVolume (thermodynamics)MathematicsGeometryMaterials sciencePhysicsStatistical physicsEngineeringComputer scienceThermodynamicsMechanical engineering

Abstract

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A planar problem of filling a silo with random in diameter disks is simulated numerically. The disks are placed statically one at a time, and each added disk results in a redistribution of normal and shear forces between the disks. It is well known that in this system microinstabilities involving local slips take place. The number of disks participating in a microslide is the result of the process of self organization, i.e., finding the stable state. As opposed to the previous research when the distribution of slide sizes (called microavalanches) was measured in a variable volume of particles, as reported by Bak et al. in 1988 and Claudin and Bouchaud in 1997, in this study the number of slips is measured and it is related to a constant volume (number of particles in the system). The statistic is collected by performing 600 fillings of the silo with the total number of particles 400, and recording the needed data after the 400th particle is dropped into the silo. The simulations are performed for the fixed coefficients of friction, distribution of particle sizes, and geometry of the boundary. The results of this study show the convergence of statistical properties for the number of slips and the energy lost to stable distributions.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.385

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 head0.181
Teacher spread0.170 · 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