Novel erosion law based on CFD–DEM simulations and its application in hydromechanical modeling of gap-graded soils
Bibliographic record
Abstract
This paper develops a novel erosion law that incorporates the influence of stress state into the mass exchange between the liquid and solid phases for suffusion, using the coupled computational fluid dynamics and the discrete element method (CFD–DEM) simulations. To achieve this, a series of CFD–DEM simulation tests are conducted on gap-graded soil samples, followed by the derivation of a new erosion law that considers the influence of seepage velocity and mechanical conditions. The proposed erosion law is then integrated into a four-constituent framework to enable hydromechanical modeling. Furthermore, a fines-dependent constitutive model based on the critical state concept is implemented to account for the influence of suffusion on the mechanical behavior of the soil. The new model is assessed through a series of laboratory hydromechanical tests, yielding satisfactory estimation results. Subsequently, the model is utilized to investigate the influence of soil initial state, including void ratio, friction angle, fine content, and size ratio, on the evolution of erosion. Finally, the mechanical behavior of soils before and after suffusion is modeled using the proposed framework. The results demonstrate that the CFD–DEM-based erosion law, as well as the hydromechanical model, effectively capture the main characteristics of soils subjected to suffusion.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".