Bibliographic record
Abstract
Rapid flows of granular materials driven by the gravity force are encountered in many geophysical contexts, which are instances of large-scale granular materials in motion.One of a famous issue about rapid flows is dam break phenomenon, which refers to the collapse of an infinite or finite volume of fluid, particles or their mixture onto a horizontal or inclined channel, where the flow is driven only by gravity.In the past, this issue attracts attention from many studies with using theoretical, experimental, and numerical investigations due to the rich flow behaviours it exhibits [1][2][3][4][5][6].In this study, the collapse of dry granular column in a horizontal chute is investigated by several lab-scale experiments.Initially, the granular column is randomly packed with two different sizes particles at one side of the chute and held by a gate.Then, the column collapses by letting the gate leaves from the column.The effect of mixture-size particles on the collapse flow of the granular columns is discussed via using several size ratios of the size-bidisperse of granular column to test in the experiments.In order to capture the particle motion when the column collapsing, a high-speed camera and PIV technology (particle image velocimetry) are used to observe the variations in the flow velocity and the flow geometry.The runout deposition is also analysed.Particle size-induced segregation in the flow has also been observed to occur spontaneously during the column collapse.Our results suggest that compositional effects in flows containing more than one particle size may be at least as important as the collapse of granular columns with single size particles.The mechanism of the granular-column-collapse flow with size-bidisperse particles can be well understood according the results in this study.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".