Rapid Wear Modelling in a Slurry Pump Using Soft 3D Impeller Material
Bibliographic record
Abstract
Slurry transport systems are often limited in run length by the life of the pump internals, especially the impeller. The present work investigated abrasive wear of the impeller of a Hayward Gordon XR2(7) Torus Recessed Impeller slurry pump in a flow loop. The stock stainless steel impeller was replaced by a set of plastic test impellers with the same nominal geometry, fabricated by additive manufacturing (3D printing). A parametric set of abrasive wear experiments was conducted at five pump rotational speeds and three solid concentrations of garnet slurry in a pipe flow loop. Pump performance tests were conducted using impellers with progressive wear conditions, to investigate how a worn impeller affects hydraulic power delivery. A parabolic fit was imposed to describe the relationship between head and flow rate, and an empirical model was proposed to predict the pump head with damaged impellers. When the rotational speed is high, the damaged impeller has a larger effect on the pump’s performance than when the rotational speed is low. The head difference between the undamaged impeller and a 7.62%-mass-loss damaged impeller was 1.5 m at 1750 rpm rotational speed, however, for 850 rpm, the head difference was 1 m. Implications for pump diagnostics in other types of systems are discussed. This experiment gives a method for rapidly assessing wear locations, and provides a tool to predict wear rates on harder materials if scaling parameters are available. This first attempt at a scaling law is not reliable enough to accurately predict the wear rate for specific conditions, but shows the relative wear as a function of pertinent parameters.
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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 | 0.001 | 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) | 0.001 | 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".