DEM modeling of one-dimensional compression of sands incorporating statistical particle fragmentation scheme
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Bibliographic record
Abstract
This paper presents a novel framework of modeling crushable granular materials under mechanical loadings based on the discrete element method (DEM). The framework is featured with the construction of the one-to-one model in which every particle in a physical experiment has its own numerical twin and allows the modeling of irregular shaped fragments during the continuous breakage process. First, image processing techniques and spherical harmonic (SH) analysis were adopted, respectively, to segment and label particles and to construct a one-to-one model mathematically in DEM. Then, a particle crushing criterion based on the maximum interparticle contact force was used to predict the crushing events, showing fitting results that agreed very well with a large number of single particle crushing tests. Next, a statistical approach for the generation of particle fragmentation modes of a given type of sand particles based on the principal component analysis (PCA) was proposed. The aim of the PCA was to analyze the statistical trends of the coefficient matrix, which was composed of the SH coefficients of all the particles involved in the analysis. Finally, a successful modeling of a particle crushing event was achieved by replacing the particle, which was judged by the crushing criterion to undergo crushing, with a few subparticles chosen randomly from a specific fragment template constructed using the microcomputed tomography (micro-CT) data.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it