Two-dimensional sub-aerial, submerged, and transitional granular slides
Bibliographic record
Abstract
The slide of granular material in nature and engineering can happen under air (subaerial), under a liquidlike water (submerged), or a transition between these two regimes, where a subaerial slide enters a liquid and becomes submerged. Here, we experimentally investigate these three slide regimes (i.e., subaerial, submerged, and transitional) in two dimensions, for various slope angles, material types, and bed roughness. The goal is to shed light on the complex morphodynamics and flow structure of these granular flows and also to provide comprehensive benchmarks for the validation and parametrization of the numerical models. The slide regime is found to be a major controller of the granular morphodynamics (e.g., shape evolution and internal flow structure). The time history of the runout distance for the subaerial and submerged cases present a similar three-phase trend (with acceleration, steady flow, and deceleration phases) tough with different spatiotemporal scales. Compared to the subaerial cases, the submerged cases show longer runout time and shorter final runout distances. The transitional trends, however, show additional deceleration and reacceleration. The observations suggest that the impact of slide angle, material type, and bed roughness on the morphodynamics is less significant where the material interacts with water. Flow structure, extracted using a granular particle image velocimetry technique, shows a relatively power-law velocity profile for the subaerial condition and strong circulations for the submerged condition. An unsteady theoretical model based on the µ(I) rheology is developed and is shown to be effective in the prediction of the average velocity of the granular mass.
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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) | 0.001 | 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".