Bibliographic record
Abstract
Granular materials are processed in many industries including agricultural, pharmaceutical, mining, and oil-sand and several geophysical processes such as landslides and avalanches. There are few models capable of predicting the flow of granular materials and particularly their collapse. Theoretical studies of the topic usually encounter difficulties in accurately predicting the collapse dynamics and final stable heaps. The two-dimensional gravitational collapse of cohesionless rectangular granular piles is numerically investigated in this paper. Piles surrounded by either air (dry case) or an oil-water mixture (wet case) undergo a dam-break collapse onto a horizontal base. The granular material is modeled as a perfectly plastic substance based on the Mohr-Coulomb law. The constitutive relations represent the granular material as a fluid, with a shear viscosity as a function of solids pressure, the second invariant of the deviatoric strain-rate tensor, and the internal angle of friction of the granular material. This two-phase flow problem (grains and liquid or air) is then formulated accordingly and solved by the mixture model method for the wet collapse and the level-set method for the dry collapse using COMSOL finite-element software. In both air and the liquid, stable heaps are achieved. The results are compared with experimental measurements of Balmforth & Kerswell [1] and Rondon et al. [2]. The model can closely predict the final shape of the collapsed dry pile. The final shape of the collapsed wet pile is also well-predicted when its initial packing concentration is relatively low. Further developments are needed to model the wet collapse of high-initial-concentration piles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".