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Record W3205720170 · doi:10.3390/w13212969

CFD-DEM Modeling of Dense Sub-Aerial and Submerged Granular Collapses

2021· article· en· W3205720170 on OpenAlexafffund
Maryam Shademani, Bruno Blais, Ahmad Shakibaeinia

Bibliographic record

VenueWater · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsPolytechnique Montréal
FundersCanada Research Chairs
KeywordsDiscrete element methodGranular materialVolume fractionCompactionComputational fluid dynamicsMechanicsGeologyVolume of fluid methodCFD-DEMMaterials scienceGeotechnical engineeringPhysicsComposite materialBreakup

Abstract

fetched live from OpenAlex

Sub-aerial (dry) and submerged dense granular collapses are studied by means of a three-phase unresolved computational fluid dynamics-discrete element method (CFD-DEM) numerical model. Physical experiments are also performed to provide data for validation and further analysis. Validations show good compatibility between the numerical and experimental results. Collapse mechanism as well as post-collapse morphological parameters, such as granular surface profile and runout distance, are analyzed. The spatiotemporal variation of solid volume fraction is also investigated. The effect granular column aspect ratio is studied and found to be a key factor in granular morphology for both submerged and dry conditions. The volume fraction analysis evolution shows an expansion and re-compaction trend, correlated with the granular movement.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.878

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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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 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

Citations13
Published2021
Admission routes2
Has abstractyes

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