The Stopping Performance of a Centrifugal Pump with Splitter Blades at Small Discharge Valve Openings

To reveal the hydraulic characteristics of a centrifugal pump with splitter blades during shutdown, a low specific speed closed impeller centrifugal pump is subjected to shutdown experiments under eight non-rated operating conditions in this paper. The transient evolution characteristics of five performance parameters with time are obtained, including rotational speed, flow rate, inlet and outlet pressures, and head. Meanwhile, the shutdown fitting models based on three machine learning models are developed. The results show that the integrated neural network model can more accurately predict the hydraulic performance of the physical pump during shutdown than the decision tree regression and random forest regression models. During the pre-mid period of the shutdown, the integrated neural network model predicts a maximum error of about 3.21% for the instantaneous flow rate and about 3.58% for the instantaneous head. This study provides a reference for the performance control of centrifugal pumps during transient operation.

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Publication Details

Journal
Processes
Published
2025-10-12
DOI
https://doi.org/10.3390/pr13103243
Primary Topic
Cavitation Phenomena in Pumps
Type
article
Field-Weighted Citation Impact
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article

The Stopping Performance of a Centrifugal Pump with Splitter Blades at Small Discharge Valve Openings

Xiaowei Xu, Xin Li, Yuliang Zhang, Jiangbo Tong
Processes
Cavitation Phenomena in Pumps
article

The Stopping Performance of a Centrifugal Pump with Splitter Blades at Small Discharge Valve Openings

Xiaowei Xu, Xin Li, Yuliang Zhang, Jiangbo Tong
article en

Abstract

To reveal the hydraulic characteristics of a centrifugal pump with splitter blades during shutdown, a low specific speed closed impeller centrifugal pump is subjected to shutdown experiments under eight non-rated operating conditions in this paper. The transient evolution characteristics of five performance parameters with time are obtained, including rotational speed, flow rate, inlet and outlet pressures, and head. Meanwhile, the shutdown fitting models based on three machine learning models are developed. The results show that the integrated neural network model can more accurately predict the hydraulic performance of the physical pump during shutdown than the decision tree regression and random forest regression models. During the pre-mid period of the shutdown, the integrated neural network model predicts a maximum error of about 3.21% for the instantaneous flow rate and about 3.58% for the instantaneous head. This study provides a reference for the performance control of centrifugal pumps during transient operation.

ProcessesVol. 13(10)
Quzhou University (CN), Hunan University of Technology (CN)
Openalex Percentile: Top 67%
Cavitation Phenomena in Pumps
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The Stopping Performance of a Centrifugal Pump with Splitter Blades at Small Discharge Valve Openings — Xiaowei Xu, Xin Li, et al. · Processes (2025) | TGRS Research Map | TGRS