Rheological testing for dam break modelling
Bibliographic record
Abstract
The rheology of tailings that have been discharged onto a tailings storage facility (TSF) and allowed to sediment out, consolidate and then undergo liquefaction is significantly higher than the rheology of the slurry in the initial discharge stream. This can be ascribed to a combination of factors that include higher solids concentration, stress state and history, re-establishment of flocculant bonds, and natural coagulation and agglomeration measurement of the rheology of the liquefied tailings therefore requires that samples undergo similar sedimentation and consolidation processes without disturbing the samples. In addition, many TSFs are constructed of two different materials, where coarser tailings are separated from the tailings stream using hydrocyclones and used to contain and confine finer tailings or applied as drainage layers. On liquefaction of the fines, the liquefied rheology will be influenced by the coarser, better-drained tailings as this is entrained with the fine tailings. Moreover, some of the supernatant water that accompanies the liquefied tailings during flow liquefaction will be entrained with the tailings, effectively diluting the liquefied slurry and impacting the rheology. All of these factors need to be considered in the course of a dam break analysis due to the influence of rheology on the fluid dynamics during flow and therefore of the resulting inundation characteristics. This paper describes laboratory and semi-pilot scale testing methods developed and applied by the authors in dam break analyses. The tests are able to address the factors of sedimentation, consolidation, stress history, material combinations and mixing, as well as supernatant water. The influence of these factors as measured in the tests is described.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".