Experimental study on the coefficient of restitution of grain against block interfaces for natural and engineered materials
Bibliographic record
Abstract
The coefficient of restitution (COR) is an important input parameter in the numerical simulation of granular flows, as it governs the travel distance, the lateral spreading, and the design of barriers. In this study, a new custom-built micromechanical impact loading apparatus is presented along with impact experiments on engineered and natural materials. The COR and energy loss of various grains and base block combinations are studied, including fairly regular-shaped Leighton Buzzard sand (LBS) grains as a natural soil and granite or rubber as base blocks, apart from the use of engineered materials for the grains (chrome steel balls, glass balls) and blocks (stainless steel, brass). The repeatability of the new micromechanical impact loading apparatus was checked by impacting chrome steel balls on stainless steel block. In all the test combinations, the higher and lower values of the COR are found for granite block (ranging between 0.75 and 0.95) and rubber block (ranging between 0.37 and 0.44) combinations, respectively. For the tested grain–block combinations, lower values of COR were observed for impacts between materials of low values of composite Young’s modulus. However, within the narrow range of composite surface roughness of the tested grain–block interfaces no particular trend was observed in the COR values. Compared to glass balls and chrome steel balls, greater scatter in the COR values is observed for natural sand grains. This is due to the variation of the elastic and morphological characteristics among individual LBS grains.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".