Mesh-free peridynamic coupled simulation of impacting collapse of a granular column with various heights
Bibliographic record
Abstract
In this study, a coupled model of Peridynamics into the mesh-free method is extended to simulate the impacting collapse of a granular column with various suspended heights. Experiments on the impacting collapse were conducted to validate the numerical model. It is found that the simulated free surface profiles have a good agreement with the experimental measurements. The numerical model is also validated by simulating the granular column collapse. The simulated kinetic energy and potential energy agree well with the discrete element method (DEM) simulation results in the literature. In the validations, different numerical particle distances are implemented to discrete the fluid and a good numerical convergence is achieved for the numerical method. After model validation, the energy variations in the impacting collapse of the granular column with different suspended heights are analyzed, which include evolutions of the potential energy, horizontal kinetic energy, and vertical kinetic energy. In the analysis, the relative total energy dissipation rate for the flow with various suspended heights is calculated. The analysis shows that the suspended height in the impacting collapse of the granular column can affect the energy dissipation significantly.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".