Analysis of surface erosion of cohesionless soils using a three-dimensional coupled computational fluid dynamics – discrete element method (CFD–DEM) model
Bibliographic record
Abstract
A fluid–solid interaction model has been implemented by coupling two numerical methods — computational fluid dynamics (CFD) and discrete element method (DEM) — that capture the mesoscale behaviors of the fluid–solid system. The model is first validated by comparing the results of simulations with two types of experiments: free settling of a single sphere in water and formation of angle of repose of particles under water, which show its capability in modeling the behaviors of both particle phase and fluid phase. The verified model is then used to study factors affecting the soil erodibility, where case studies are designed for soil particles deposited inside a pipe and subsequently subjected to water flow–induced surface erosion. Influencing factors for soil erodibility, including particle diameter and interparticle bond, are analyzed. For cohesionless soils without bond strength, the critical shear stress is found to be linearly related to particle size; while for soils with bond strength, simulation results show that interparticle bonding largely decelerates the erosion process and causes a much lower erosion rate. To further the understanding of soil surface erosion under turbulent flow, the “k–ε” turbulence model has been successfully implemented for the fluid phase. Comparison between the laminar model and the turbulence model shows turbulence significantly accelerates the erosion process.
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 | 0.000 |
| 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 | 0.001 | 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".