A coupled CFD–DEM investigation of internal erosion considering suspension flow
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 F c (i.e., the percentage by mass of the fine particles in the gap-graded sample). The influences of C and F c 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 F c = 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.
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.
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 it