Wear Rate Prediction in Multi-Size Particulate Flow Through Impellers
Bibliographic record
Abstract
A three-dimensional finite element formulation of multi-size particulate flow through a centrifugal impeller is presented. From the predicted flow field, the wear rate distribution along the impeller blades and shrouds is calculated using empirically determined impact and sliding wear coefficients. The computational domain consists of the three dimensional region enclosed between the two blades and the two shroud surfaces with upstream and downstream extensions. An Eulerian-Eulerian mixture model is used for the multi-size particulate flow, which consists of mixture continuity and momentum equations as well as the individual solids continuity and momentum equations.The effects of flow operating conditions on the wear rates are studied. A comparison of predicted wear rates from D50 or other mono-size simulations with multi-size particulate flow simulations shows that representing the slurry by a single particle size could result in substantial errors for different flow conditions.Copyright © 2013 by ASME
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".