Numerical Investigation on the Impact of Tailings Slurry on Catch Dams Built at the Downstream of a Breached Tailings Pond
Bibliographic record
Abstract
Tailings storage facilities (TSFs) are known as a time-bomb. The numerous failures of TSFs and the heavy catastrophic consequences associated with each failure of TSFs indicate that preventing measures are necessary for existing TSFs. One of the preventing measures is to construct catch dams along the downstream near TSFs. The design of catch dams requires a good understanding of the dynamic interaction between the tailings slurry flow and the catch dams. There are, however, very few studies on this aspect. In this study, a numerical code, named LS-DYNA, that is based on a combination of smoothed particle hydrodynamics and a finite element method, was used. The numerical modeling shows that the tailings slurry flow can generally be divided into four stages. In terms of stability analysis, a catch dam should be built either very close to or very far from the TSF. When the catch dam with an upstream slope of a very small inclination angle is too close to the tailings pond, it can be necessary to build a very high catch dam or a secondary catch dam. As the impacting force can increase and decrease with the fluctuations back-and-forth of the tailings slurry flow, the ideal inclination angle of the upstream slope of the catch dam is between 30° and 37.5°, while the construction of a catch dam with a vertical upstream slope should be avoided. However, a catch dam with steeper upstream slopes seems to be more efficient in intercepting tailings flow and allowing the people downstream to have more time for evacuation. All these aspects need to be considered to optimize the design of catch dams.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".