Investigation of granular flow run-out behavior under dilative and compressive Coriolis conditions using DEM simulation
Bibliographic record
Abstract
Centrifuge modeling allows granular flows to be simulated under stresses characteristic of full-scale flows, while maintaining repeatability. However, the impact of the Coriolis effect on the run-out behavior of dry granular flows is not fully understood. In this study, using the discrete element method (DEM), we conducted simulations of dry granular flow both with and without Coriolis (dilative and compressive) conditions to analyze the impact of the Coriolis effect on granular run-out mobility, flow structure, and granular interaction. For unsteady flows, the dilative Coriolis force increased the moving distance of the flow centroid by 60%–70% and increased the maximum kinetic energy by 5%–17%, whereas those parameters were reduced by 30% and 2%–9%, respectively, under compressive Coriolis conditions. Results showed that selecting a lower centrifugal acceleration by reducing the rotational angular velocity in physical modeling is ineffective for realizing a weaker Coriolis effect. This study established that using a larger centrifuge could mitigate the Coriolis effect, but that this outcome became less notable as the centrifugal radius increased. Additionally, we suggest a preliminary relation that could be used to correct the results of experimental granular flow final run-out distance obtained using a geotechnical centrifuge.
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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) | 0.001 | 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".