Effective Particle Size Representation for Erosion Wear in Centrifugal Pump Casings
Bibliographic record
Abstract
For the purpose of Computational Fluid Dynamic (CFD) simulations, the broad particle size distribution (PSD) encountered in industrial slurries is classified into a discrete number of size classes. Since mono-size simulations consume much less computational time, especially in 3D simulations, it would be advantageous to determine an equivalent single particle size representation which yields the same wear distribution predictions as the multi-size simulations. This work extends the previous two-dimensional study [1], which was for a specific PSD slurry flow through three selected pumps, to determine an effective equivalent mono-size representation. The current study covers two-dimensional simulations over a wide range of pumps of varying sizes (40 pumps), 2 inlet concentrations and 4 different particle size distributions. Comparison is made between the multi-size wear prediction and different possible representative mono-size particle wear predictions. In addition, a comparison of multi-size and different mono-size results using three dimensional simulations is also shown for a typical slurry pump as a sample case to highlight that the conclusions drawn for two dimensional simulation could hold good for three dimensional simulations as well. It is observed that by using a mono-size equivalent, the computation time is 20–25% of the computation time for multi-size (6-particle) simulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".