LAPSE:2021.0116
Published Article
LAPSE:2021.0116
Multi-Condition Optimization of Cavitation Performance on a Double-Suction Centrifugal Pump Based on ANN and NSGA-II
Wenjie Wang, Yanpin Li, Majeed Koranteng Osman, Shouqi Yuan, Benying Zhang, Jun Liu
March 14, 2021
Double-suction centrifugal pumps form an integral part of power plant systems in maintaining operational stability. However, there has been a common problem of achieving a better cavitation performance over a wider operating range because the traditional approach for impeller design often leads to the design effect not meeting the operational needs at off-design conditions. In addressing the problem, an optimization scheme was designed with the hub and shroud inlet angles of the double-suction impeller to minimize the suction performance at non-design flow conditions. A practical approach that speeds up the cavitation simulation process was applied to solve the experimental design, and a multi-layer feed forward artificial neural network (ANN) was combined with the non-dominated sorting genetic algorithm II to solve the multi-objective problem into three-dimensional (3D) Pareto optimal solutions that meet the optimization objective. At the design point, the suction performance was improved by 6.9%. At non-design flow conditions, the cavitation performance was improved by 3.5% at 1.2Qd overload condition, 4% at 0.8Qd, and 5% at 0.6Qd. Additionally, there was significant reduction in the attached cavity distribution in the impeller and suction domains when the optimized model was compared to the original model at off-design points. Finally, the optimization established a faster method for a three-objective optimization of cavitation performance using ANN and 3D Pareto solutions.
Keywords
artificial neural networks (ANN), cavitation performance, double suction, multi-condition optimization, net positive suction head (NPSH)
Suggested Citation
Wang W, Li Y, Osman MK, Yuan S, Zhang B, Liu J. Multi-Condition Optimization of Cavitation Performance on a Double-Suction Centrifugal Pump Based on ANN and NSGA-II. (2021). LAPSE:2021.0116
Author Affiliations
Wang W: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China [ORCID]
Li Y: School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Osman MK: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China; Department of Mechanical Engineering, Wa Technical University, Wa XW-0547-6186, Ghana [ORCID]
Yuan S: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China
Zhang B: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China
Liu J: Shandong Shuanglun Co. Ltd., 6 Dongxin Road, Weihai 264203, China
Journal Name
Processes
Volume
8
Issue
9
Article Number
E1124
Year
2020
Publication Date
2020-09-10
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8091124, Publication Type: Journal Article
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LAPSE:2021.0116
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doi:10.3390/pr8091124
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Mar 14, 2021
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Mar 14, 2021
 
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Calvin Tsay
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