Effects of curvature-related DEM contact model on the macro- and micro-mechanical behaviours of granular soils
Bibliographic record
Abstract
A comprehensive comparison between the Hertz–Mindlin model and the linear spring model in true triaxial shear simulations of granular soils was conducted using the discrete-element method (DEM). The no-slip Hertz–Mindlin model for general elastic non-spherical particles with smooth surfaces was revisited and implemented for superellipsoidal particles in an in-house DEM code. Three groups of specimens with a grain size distribution of Ottawa 20–30 sands, consisting of spheres, ellipsoids and superellipsoids, respectively, were subjected to triaxial shear DEM simulations with the Hertz–Mindlin model and the linear spring model. The corresponding mechanical behaviours were examined in terms of a series of macro- and micro-parameters. It was found that the linear spring model was able to resemble the Hertz–Mindlin model in aspects of both microscopic and macroscopic mechanical behaviours of granular media with spherical and/or non-spherical particles. This finding suggests that the linear spring model can be used to investigate micro-mechanical behaviours of granular soils, even with complex particle shapes.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".