Structural Optimization of Jet Fish Pump Design Based on a Multi-Objective Genetic Algorithm

Maosen Xu, Guorui Zeng, Dazhuan Wu, Jiegang Mou, Jianfang Zhao, Shuihua Zheng, Bin Huang and Yun Ren
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Maosen Xu: College of Energy Engineering, Zhejiang University, Hangzhou 310027, China
Guorui Zeng: College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China
Dazhuan Wu: College of Energy Engineering, Zhejiang University, Hangzhou 310027, China
Jiegang Mou: College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China
Jianfang Zhao: Zhejiang Nanyuan Pump Industry Co., Ltd., Huzhou 313219, China
Shuihua Zheng: School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Bin Huang: School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Yun Ren: Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, China

Energies, 2022, vol. 15, issue 11, 1-16

Abstract: Jet fish pumps are efficient hydraulic machinery for fish transportation. Yet, the complex flow phenomenon in it is the major potential risk for damage to fish. The dangerous flow phenomena for fish, such as radial pressure gradient and exposure strain rate, are usually controlled by the structural parameters of jet fish pumps. Therefore, the injury rate of fish can be theoretically decreased by the structural optimization design of jet fish pumps. However, there is a complex nonlinear relation between flow phenomena and key structural parameters. To solve this problem, the present paper established a complex mapping between flow phenomena and structural parameters, based on computational fluid dynamics and a back-propagation neural network. According to this mapping, an NSGA-II multi-objective genetic algorithm was used to optimize the structure of jet fish pumps. The results showed that the optimized jet fish pumps could reduce the internal radial pressure gradient, exposure strain rate and danger zone to 40%, 12.5% and 50% of the pre-optimization level, respectively. Therefore, the optimized jet fish pump could significantly reduce the risk of fish injuries and keep the pump efficiency at a high level. The results could provide a certain reference for relevant structural optimization problems.

Keywords: jet fish pump; structural optimization; pressure gradient; exposure strain rate; BP neural network; NSGA-II algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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