Deposition Modelling of High Density Tailings Using Smoothed Particle Hydrodynamics
Bibliographic record
Abstract
High density (HD) tailings are tailings that have been sufficiently dewatered, where they exhibit a yield stress upon deposition, and therefore naturally form gently sloped deposits that do not requires dams for containment.It is essential to comprehend and model the flow behaviour during deposition to predict the final geometry of the stack and control storage capacity; which are important design elements to HD tailings technology.As HD tailings exhibit a yield stress, modelling stack geometry constitutes, in part, a problem of non-Newtonian flow with a free surface.This research investigated modelling the flow behaviour of HD tailings, using an open-source Smoothed Particle Hydrodynamics (SPH) code.The results indicated that two-dimensional simulations using SPH agreed well with experimental data for single and multi-layer flume tests.SPH has the advantage over simpler methods, such as Lubrication Theory, as SPH better predicts the geometry when inertia influences the flow of tailings.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".