Probing Shear Stress Distribution within Single Particle Scale inside Particulate Packing
Bibliographic record
Abstract
Studies have been performed to understand the nature of stress distribution within birefringent sensor particles embedded inside a granular bed under axial compression loading. Both the variation of the maximum shear stress and the direction of the major principal stress within single particle scale are analysed with respect to the proximity of particles to the wall boundaries of the compression chamber. The study shows that for an increase in the loading intensity, multiple interactions of contacts result in non-homogeneous distribution of maximum shear stress within sensor particles. The ability of the particles to sustain maximum shear stress depends on how closely these reside with respect to wall boundaries. These results imply that the applicability of present contact interaction laws used in advanced simulation methods such as the Discrete Element Method (DEM) for modelling the mechanical behaviour of micro and nano particulate assemblies could be limited and needs to be revised. This is because DEM modelling is normally based on the assumption that the interaction behaviour of a given particle contact is independent of what happens at its neighbouring contacts. Though further studies are required, the current research is a step towards attaining a clear understanding of the mechanical response of particulate materials under industrial process loading conditions which is rather complex as of now.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".