Field Scale Flow Modeling of Thixotropic Mine Tailings Using the Material Point Method
Bibliographic record
Abstract
Understanding free surface flow of mine tailings is necessary to plan deposition operations in tailings' storage facilities (TSF), and is important to predict the consequences of a dam break.Proper understanding of deposition operations has probably hindered adoption of alternative tailings technologies, wheras recent failures of tailings impoundments have had catastrophic consequences.Flow behavior is crucial to understand for effective risk mitigation.Tailings exhibit thixotropic properties -their behavior is a function of the material's stress history.A thixotropic constitutive model has been implemented into an open-source Material Point Method (MPM) framework.The results were validated with previous experimental data at laboratory scale and compared with Bingham rheology with good agreements.Large-scale simulations were done for 2D and 3D cases.The thixotropic model was able to model self-forming channels seen during field scale deposition, which Bingham rheology is unable to capture.Dam break events also scaled differently from laboratory scale experiments with thixotropy.This can possibly be an element that explains longer runouts then initially predicted with standard rheology.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".