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Record W2900338474

Particle Scale Dynamics of Coarse Granular Material

2018· dissertation· en· W2900338474 on OpenAlexaboutno aff
Kyle Williams

Bibliographic record

VenueMinds at UW (University of Wisconsin) · 2018
Typedissertation
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
Fundersnot available
KeywordsParticle dynamicsDynamics (music)Scale (ratio)Particle (ecology)Statistical physicsMaterials scienceGranular materialChemical physicsPhysicsMolecular dynamicsComposite materialChemistryGeologyComputational chemistry
DOInot available

Abstract

fetched live from OpenAlex

Developing a new wheeled or tracked vehicle for military use typically involves physical\nprototyping of preliminary vehicle designs and field tests to evaluate their performance in a\nrange of conditions. Computational simulations of vehicle performance, including physics-based\nsimulation of vehicle-terrain interaction, is an effective alternative for reducing costs of physical\nprototyping. Activities described in this thesis have been conducted in coordination with the\nSimulation Based Engineering Laboratory (SBEL) at the University of Wisconsin-Madison,\nwhich is actively engaged in physics-based simulation of vehicle-terrain interaction for military\nand other vehicles. The primary tool used for simulation is the Discrete Element Model (DEM)\nChrono::Engine and the Chrono::Granular toolkit (Mazhar, et al., 2013).\nThe primary objective of this research was to provide a robust physical data set to\nvalidate Chrono::Granular simulations of coarse-grained granular material behavior using\nrelatively simple and common geotechnical tests. Tests selected included laboratory fall cone\ntests and direct shear tests using dry and moist preparations of 20-30 Ottawa sand and 3 mm\nspherical glass beads. A special direct shear device was designed, constructed, and calibrated to\nprovide direct visual observation of particle displacements for a single plane of particles subject\nto shearing. Shear stress-displacement relationships measured during shearing and individual\nparticle displacements tracked using particle image velocimetry (PIV) were made available for\ncomparison with discrete particle displacements simulated by DEM and Chrono::Granular.\nResults from the fall cone testing series, including measurements of cone penetration depth as a\nfunction of time, cone apex angle, drop height, and initial particle density were also made\navailable for direct comparison with computational simulations. The suite of fall cone and direct\nshear test results was analyzed to investigate relationships among the testing parameters. A case\nstudy review of similarly unconventional applications of fall cone and direct shear testing and of\napplications of PIV to granular material is provided.\nResults from the fall cone penetration test series indicate that penetration into coarse\ngranular media is most affected over small ranges of drop height by the cone geometry (apex\nangles of 30° and 60°). Results from the direct shear test series indicate that shear displacement\nrate affects individual particle motion, with lower shear rates allowing more rotation and\nlocalized particle displacements than higher shear rates. Introducing water into the shear zone\nchanges bulk shear strength and the character of individual particle motions. Direct shear tests\nwith glass beads indicated that capillary bridges between particles caused displacement of\nparticles in zones that had not moved in dry tests. Direct shear tests with sands indicated that the\ninclusion of water near the shear zone caused shear failure to occur outside the wetted zone along\na different shear surface. Shear band development was observed to depend on shear displacement\nrate, applied normal force, and the presence or absence of moisture in the shear surface. Direct\ncomparisons between the physical test results reported here and analog Chrono::Granular\nsimulations not included as part of this thesis.

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 categoriesMeta-epidemiology (narrow)
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 score0.069
Threshold uncertainty score1.000

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)0.0010.000

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 head0.171
Teacher spread0.166 · 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.

Study designBench or experimental
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

Citations1
Published2018
Admission routes1
Has abstractyes

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