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Optimization of impulse water turbine based on GA-BP neural network arithmetic

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Abstract

To develop an optimum design method for impulse water turbines with low specific speed, a representative impulse water turbine with low specific speed used in agricultural irrigation machinery was optimized with a combination of an orthogonal experimental design, a genetic algorithm, and a BP neural network in this study. Numerical calculation was applied to analyze interflow characteristics for optimized and original water turbines. Results showed that the internal flow characteristics of the optimized water turbine presented remarkable improvement compared with the original water turbine. Pressure distribution increased, the vortex strip in the draft tube was reduced remarkably, and impeller torque increased by 26 %. In addition, the optimized impeller was manufactured by 3D printing, and performance comparison was conducted between experiments of the optimized and original water turbines. The efficiency of the optimized water turbine reached 42.5 %, which exceeded the original water turbine’s of 8.5 %. With increasing rotating speed, maximum efficiency running point moved to a high flow rate, and highly efficient areas expanded. Internal characteristic analysis and a full-scale experiment for both water turbines showed that the performance of the optimized water turbine exhibited substantial improvement. The analysis and experiment also verified the theoretical correctness and feasibility of the proposed optimum design method.

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Authors and Affiliations

  1. Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang, 212013, China

    Lingdi Tang, Shouqi Yuan & Yue Tang

  2. Jiangsu Huayuan Water-saving Ltd., Xuzhou, 221150, China

    Zhipeng Qiu

Authors
  1. Lingdi Tang
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  2. Shouqi Yuan
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  3. Yue Tang
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  4. Zhipeng Qiu
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Corresponding author

Correspondence to Lingdi Tang.

Additional information

Recommended by Associate Editor Weon Gyu Shin

Lingdi Tang is an Assistant Professor at the National Research Center of Pumps, Jiangsu University. Her research interests include optimization design and unsteady flow in fluid machinery. She received her Ph.D. degree from Jiangsu University in 2017.

Shouqi Yuan is a Professor at the National Research Center of Pumps, Jiangsu University. His research interests include theory and optimization design of fluid machinery. He received his Ph.D. degree from Jiangsu University in 1994.

Yue Tang is a Professor at the National Research Center of Pumps, Jiangsu University. His research interests include analysis of dynamic characteristics for fluid machinery and control technology of fluid machinery.

Zhipeng Qiu is a President of Jiangsu Huayuan Water Saving Co., Ltd. in China. He is engaged in water-saving equipment research, development, and manufacturing.

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Tang, L., Yuan, S., Tang, Y. et al. Optimization of impulse water turbine based on GA-BP neural network arithmetic. J Mech Sci Technol 33, 241–253 (2019). https://doi.org/10.1007/s12206-018-1224-3

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  • DOI: https://doi.org/10.1007/s12206-018-1224-3

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