Influence of impeller-tongue interaction on the unsteady cavitation behavior in a centrifugal pump
Bibliographic record
Abstract
Purpose – The purpose of this paper is to analyze the cavitation dynamics in the blade channel of a centrifugal pump with a particular focus on the direct influence of the pump’s volute. Design/methodology/approach – A homogeneous multiphase model, namely the Zwart-Gerber-Belamri cavitation model, is employed to numerically describe the evolution of the process of cavitation within the pump. The RNG k-e turbulence model is applied to analyze the unsteady turbulent flow. A second order implicit formulation is used for the time discretization for the unsteady flow calculation and a finite volume algorithm is used for the space discretization. Findings – The cavities in the passage exhibit an obvious life cycle which includes initiation, growth, contraction, and separation, and collapse with a frequency corresponding to the impeller rotation frequency under off-design conditions. This phenomenon arises through an alternating interaction between reverse flow with the cavity interface and is associated with the response of the vortex region to the effect of uneven pressure distribution on volute and impeller-tongue interaction. Originality/value – This study simulated and analyzed the complex transient cavitation flow patterns inside a centrifugal pump and explains the reason for the unsteadiness. This knowledge is instructive in achieving the stable operation of pumps and in trouble shooting rough or cavitating operation.
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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 | 0.000 |
| 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) | 0.001 | 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".