A coupled CFD–DEM investigation of internal erosion considering suspension flow
Bibliographic record
Abstract
The influence of two-phase flows containing suspension particles, which are common in nature, on internal erosion with coupling effect of clogging remains unclear. This paper presents a three-dimensional coupled computational fluid dynamics and discrete element method (CFD–DEM) analysis of internal erosion considering different concentrations of suspension C (i.e., mass of the suspended particles in unit volume of fluid) in gap-graded granular soils with different fine fraction Fc (i.e., the percentage by mass of the fine particles in the gap-graded sample). The influences of C and Fc on the erosion and clogging behavior of soils are investigated from both the macroscopic and microscopic perspectives. It is found that for gap-graded samples being underfilled with Fc = 15%, the suspension flow (i.e., influent fluid with suspending particles) decreases the cumulative eroded fine particle loss and the increasing rate of soil hydraulic conductivity due to clogging at the top of the sample. The degree of clogging is found to jointly be determined by both constriction size distribution and the suspension concentration. Clogging in a local area usually occurs with the formation of the clusters, which have a high resistance to the drag force applied by the fluid 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".