2-D Pore-Particle Scale Model of the Erosion at the Boundary of Two Soils under Horizontal Groundwater Flow
Bibliographic record
Abstract
Modeling particle-fluid systems in porous media encountered in many scientific and engineering applications presents a significant challenge. This paper outlines a hydrodynamic flow at a low Reynolds number through saturated porous media which is generated virtually using only the grain-size distribution curves of soils. In order to represent the pore and granular structures of soils, a novel model was developed using a fractal approach. For a given porosity, particle and pore size distributions were successfully modeled for a wide range of soils. We tested the model's conductivity behaviour by carrying out a steady-state flow through a fractal structure. Soil particle movement was modeled using the discrete element method (DEM), and the hydrodynamic flow was simulated by the well-known marker-and-cells (MAC) method, with special conditions imposed at the particle boundaries. The fluid-particle interaction was taken into account in our calculations using the method of direct force integration at the surface of particles. Several comparisons of our numerical results to those of published experiments for particle-particle and particle-wall interaction in a viscous fluid show very good agreement. Finally, an example of a possible erosion scenario at the interface of two different soils under a horizontal flow is presented. The results and a discussion of this model's applicability are also presented. This study is a part of an extensive program which includes 3-D simulations aimed at gaining a better understanding erosion phenomena in soils made up of irregularly shaped particles under hydrodynamic flow.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".