Modelling of cavitation in a high‐intensity agitation cell
Bibliographic record
Abstract
Abstract Small‐scale air bubbles, introduced via cavitation, enhance particle flotation in a high‐intensity agitation (HIA) cell. In this work, the local energy dissipation rates which govern the cavitation phenomena in a two‐baffled HIA cell with several different impellers were studied using computational fluid dynamics. The simulation predicts that a 4‐vane flat blade turbine dissipates more power than other turbines tested in the given HIA cell. A cavitation model is used in conjunction with a multiphase mixture model to predict the vapour generation. Cavitating flow is simulated at different RPM, dissolved gas concentration, and temperatures. Predicted volume fraction of vapour showed a strong dependency on operating conditions. For comparative study, cavitation in a pressure driven flow through a constriction is also modelled. A population balance model is used to obtain the bubble size distribution of the generated cavities in an orifice flow. The method can be extended to characterise the bubble size distribution in HIA cells.
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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".