Particle Scale Anisotropy Controls Bulk Properties in Sheared Granular Materials
Bibliographic record
Abstract
The bulk dynamics of dense granular materials arise through a combination of particle-scale and mesoscale effects. Theoretical and numerical studies have shown that collective effects are created by particle-scale anisotropic structures such as grain connectivity, force transmission, and frictional mobilization, all of which influence bulk properties like bulk friction and the stress tensor through the stress-force-fabric (SFF) relationship. To date, establishing the relevance of these effects to laboratory systems has remained elusive due to the challenge of measuring both normal and frictional contact forces at the particle scale. In this study, we perform experiments on a sheared photoelastic granular system in a quasi-2D annular cell. During these experiments, we measure particle locations, contacts, and normal and frictional forces vectors during loading. We reconstruct the angular distributions of the contact and force vectors, and extract the corresponding emergent anisotropies for each of these metrics. Finally, we show for the first time in an experimental system that the SFF relation quantitatively predicts the relationship between particle scale anisotropies, the stress tensor components, and the bulk friction coefficient, capturing even transient behaviors-closing the gap between experimental measurements and prior theoretical and numerical models.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".