Froude characterization for unsteady single-surge dry granular flows: impact pressure and runup height
Bibliographic record
Abstract
The impact and pileup mechanisms of unsteady granular flows impacting a rigid barrier are governed by the Froude conditions (Fr). Velocity and depth vary along the length of the flow. There is currently no widely accepted approach for characterizing Fr for impact and runup problems. In this study, a discrete element method (DEM) model was calibrated against a physical flume test. Eighty-six simulations were performed using the DEM model to investigate the equivalent Fr governing pileup height and impact pressure for unsteady single-surge dry granular flows against a rigid barrier. Fr and the grain diameter were varied. Results reveal that Fr within the frontmost 5% of a flow governs both pileup height and impact pressure. Thus, taking frontal velocity and maximum flow depth within the frontmost region is crucial for properly characterizing the runup height and impact load. Consistent characterization of Fr is possible near the longitudinal centre of a flow; the frontmost Fr can then be extrapolated from calibration curves. Results imply that existing studies that predict impact pressure based on nonfrontal Fr values may underestimate impact pressure by a factor of up to 2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".