Continuum-Based Approach to Model Particulate Soil–Water Interaction: Model Validation and Insight into Internal Erosion
Bibliographic record
Abstract
Resolving the interaction between soil and water is critical to understanding a wide range of geotechnical applications. In cases when hydrodynamic forces are dominant and soil fluidization is expected, it is necessary to account for the microscale interactions between soil and water. Some of the existing models such as coupled Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) can capture microscale interactions quite accurately. However, it is often computationally expensive and cannot be easily applied at a scale that would aid the design process. Contrastingly, continuum-based models such as the Two-Fluid Model (TFM) can be a computationally feasible and scalable alternative. In this study, we explored the potential of the TFM to simulate granular soil–water interactions. The model was validated by simulating the internal fluidization of a sand bed due to an upward water jet. Analogous to leakage from a pressurized pipe, the simulation was compared with the available experimental data to evaluate the model performance. The numerical results showed decent agreement with the experimental data in terms of excess pore water pressure, fluidization patterns, and physical deformations in violent flow regimes. Moreover, detailed soil characteristics such as particle size distribution could be implemented, which was previously considered a shortcoming of the model. Overall, the model’s performance indicates that TFM is a viable tool for the simulation of particulate soil–water mixtures.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".