DEM investigation into effects of polydispersity of multilevel morphology on shear behaviors of granular materials
Bibliographic record
Abstract
Extensive efforts have been made to investigate the multilevel shape dependence of the shear behaviors of granular materials. However, the impact of shape polydispersity at each morphological level, inherent in realistic granular materials, remains understudied. This study aims to address this fundamental issue. The concept of the overall polydispersity index (OPI) is utilized to quantify polydispersity degrees in elongation index (EI), flatness index (FI), angularity index (AI), and roughness (RG), denoted as OPIEI, OPIFI, OPIAI, and OPIRG, respectively. Four series of granular assemblies with varying OPIEI, OPIFI, OPIAI, and OPIRG values are generated and employed in discrete element method simulations to prepare samples for triaxial shearing. The variation trends of both macroscopic and microscopic behaviors, as well as fabric anisotropy influenced by OPIEI, OPIFI, OPIAI, and OPIRG are analyzed in detail. These results are also compared with those from monodisperse samples to highlight the notable effects of particle shape polydispersity on shear behavior. Additionally, a prediction model for the shear behaviors of polydisperse samples is proposed by averaging the contributions of all constituent particle shapes within the sample. This model can serve as an effective bridge between shear behaviors of polydisperse and monodisperse samples.
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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 | 0.000 |
| 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 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".