Computed tomography-driven analysis of particle breakage using a coupled FDM–DEM approach
Bibliographic record
Abstract
This study proposes a refined approach for simulating the mechanical behavior of soils under high stress by integrating the finite difference method–discrete element method (FDM–DEM) with in situ experiment. This novel framework incorporates advanced technologies such as flexible membrane and X-ray computed tomography (CT) to enhance the predictive capabilities of the simulation with physical insight. The FDM–DEM model adeptly simulates irregular particle shapes, capturing their interactions and the dynamics of particle breakage. The use of flexible membrane within the model further enriches this approach by enabling the capture of deformation responses in granular materials during shearing. Moreover, the adoption of CT technology facilitates continuous optimization of the simulation process. Validation through in situ experiments has confirmed the model’s effectiveness. The ability of the model to predict areas of high stress concentration—and their correlation with observed particle breakage patterns—underscores the utility of the enhanced FDM–DEM framework as a robust predictive tool for understanding the behavior of granular materials under shearing.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".