A coarse graining CFD–DEM simulation on contact erosion of layered cohesionless soils
Bibliographic record
Abstract
A coarse graining computational fluid dynamics–discrete element method (DEM) model is developed to analyze the contact erosion of cohesionless soils at the coarse/fine soil interface. Two benchmark cases are provided to verify the model implementation, including the collision of two groups of particles and collapse of a soil column in water. It is demonstrated the CG approach significantly reduces the computational cost with sufficiently good accuracy. Two typical CG approaches, i.e., the relative overlap scaling (ROS) and absolute overlap scaling approaches, are compared and show similar model performance. While the DEM time step of ROS approach is scaled up by the scaling factor, it is recommended in this study for the better computational performance. The verified model is used to simulate the laboratory contact erosion experiment under various flow conditions. The critical discharge rate calculated from the numerical model is 357 mL/s, which achieves a high consistency with the experimental value of 311 mL/s. Further study shows the scaling factor influences the critical discharge rate, as large parcels introduce boundary effects especially for coarse soils. It is also shown the packing state of coarse soils is a determinant in the erosion initiation, as the void fraction distribution is the key for particle and fluid transports, especially near the contact surface.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".