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

Author

Listed:
  • Maosen Xu

    (College of Energy Engineering, Zhejiang University, Hangzhou 310027, China
    College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, 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)

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.

Suggested Citation

  • Maosen Xu & Guorui Zeng & Dazhuan Wu & Jiegang Mou & Jianfang Zhao & Shuihua Zheng & Bin Huang & Yun Ren, 2022. "Structural Optimization of Jet Fish Pump Design Based on a Multi-Objective Genetic Algorithm," Energies, MDPI, vol. 15(11), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4104-:d:830654
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    References listed on IDEAS

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    1. Huican Luo & Peijian Zhou & Lingfeng Shu & Jiegang Mou & Haisheng Zheng & Chenglong Jiang & Yantian Wang, 2022. "Energy Performance Curves Prediction of Centrifugal Pumps Based on Constrained PSO-SVR Model," Energies, MDPI, vol. 15(9), pages 1-19, May.
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    Cited by:

    1. Wenshuang Zhao & Nan Jiang & Yuanyuan Wei & Xinke Zhao, 2023. "Geo-Economic Analysis Based on an Improved Ant Colony Optimization," Sustainability, MDPI, vol. 15(6), pages 1-25, March.
    2. Lei Wang & Jiayi Cui & Lingfeng Shu & Denghui Jiang & Chun Xiang & Linwei Li & Peijian Zhou, 2022. "Research on the Vortex Rope Control Techniques in Draft Tube of Francis Turbines," Energies, MDPI, vol. 15(24), pages 1-27, December.
    3. Grzegorz Filo, 2023. "Artificial Intelligence Methods in Hydraulic System Design," Energies, MDPI, vol. 16(8), pages 1-19, April.

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