Modeling the influence of particle morphology on the fracture behavior of silica sand using a 3D discrete element method
Bibliographic record
Abstract
The constitutive behavior and deformation characteristics of uncemented granular materials are to a large extent derived from the fabric or geometry of the particle structure and the interparticle friction resulting from normal forces acting on particles or groups of particles. Granular materials consist of discrete particles with a fabric (microstructure) that changes under loading. Synchrotron micro-computed tomography (SMT) has emerged as a powerful non-destructive 3D scanning technique to study geomaterials. In this paper, SMT was used to acquire in situ scans of the oedometry test of a column of three silica sand particles. The sand is known as ASTM 20–30 Ottawa sand, and has a grain size between US sieves #20 (0.841 mm) and #30 (0.595 mm). The characteristics and evolution of particle fracture in sand were examined using SMT images, and a 3D discrete element method (DEM) was used to model the fracture behavior of sand particles. It adopts the bonded particle model to generate a crushable agglomerate that consists of a large number of small spherical sub-particles. The agglomerate shape matches the 3D physical shape of the tested sand particles by mapping the particle morphology from the SMT images. The paper investigates and discusses the influence of agglomerate packing (i.e., the number and size distribution of spherical sub-particles that constitute the agglomerate) and agglomerate shape on the fracture behavior of crushable particles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".