Impact of splitter blades on the wall wear characteristics of centrifugal pumps at low solid phase concentration
Bibliographic record
Abstract
Splitter-blade impellers are increasingly being adopted in centrifugal pumps due to their superior performance. However, their service life is significantly reduced when handling fluids containing even small amounts of sediment. To investigate the wear characteristics of splitter-blade centrifugal pumps under solid-liquid two-phase flow conditions, a combined approach of numerical simulation and experimental study was employed to analyze wall wear under low solid-phase particle concentrations. The results demonstrate that the numerical simulations meet the required accuracy for analysis, with maximum relative errors of 4.8% for head and 3.4% for efficiency. The head curve of the centrifugal pump equipped with splitter blades is generally higher than that of the pump without splitter blades under both single-phase and solid-liquid two-phase flow conditions. Notably, under low-flow-rate conditions in the solid-liquid two-phase flow, the impeller with splitter blades demonstrates better head stability. Although the introduction of splitter blades alters the flow field structure at the impeller inlet to some extent, it also enhances the uniformity of pressure and velocity distribution in the circumferential direction at the impeller outlet. The presence of splitter blades primarily affects the wear characteristics on the pressure side of the blades and the middle and rear regions of the impeller rear shroud, resulting in reduced wear. However, as the solid particle concentration increases, impeller wear becomes more severe, suggesting that this type of centrifugal pump should be avoided in high-concentration conditions. Therefore, impellers with splitter blades exhibit certain advantages in transporting fluids with low solid-phase concentrations. This study provides important theoretical insights and data support for the hydraulic optimization of centrifugal pump structures.
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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 | 0.001 | 0.002 |
| 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 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".