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Multiparameter optimization for the nonlinear performance improvement of centrifugal pumps using a multilayer neural network

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Abstract

To increase efficiency at the design point of a centrifugal pump, this study adopted an artificial neural network in the construction of an accurate nonlinear function between the optimization objective and the design variables of impellers. Modified particle swarm optimization was further applied to refine the mathematical model globally. The database, which consisted of 200 sets of impellers, were generated from the Latin hypercube sampling method, and their corresponding efficiencies were obtained automatically from numerical simulation. Design variables were the distributions of blade angles, and results established that the difference between the numerical performance curve and the experimental results was acceptable. Optimization with a two-layer feedforward network improved the pump efficiency at the design point by 0.454 %. Flow complexity improved as the blade curvature increased. The application of the multilayer neural network could provide a meaningful reference to single- and multi-objective optimization of complex and nonlinear pump performance.

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Acknowledgments

This work is supported by the National Key Research and Development Program (Grant No. 2018YFB0606103), National Natural Science Foundation of China (Grant Nos. 51879121, 51779107), China Postdoctoral Science Foundation funded project (Grant No. 2019M651736), Six Talent Peaks Project (GDZB-047) and Qing Lan Project of Jiangsu Province.

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Authors and Affiliations

  1. National Research Center of Pumps, Jiangsu University, Zhenjiang, 212013, China

    Ji Pei, Wenjie Wang, Majeed Koranteng Osman & Xingcheng Gan

  2. Mechanical Engineering Department, Wa Polytechnic, Wa, Ghana

    Majeed Koranteng Osman

Authors
  1. Ji Pei
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  2. Wenjie Wang
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  3. Majeed Koranteng Osman
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  4. Xingcheng Gan
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Corresponding author

Correspondence to Wenjie Wang.

Additional information

Recommended by Associate Editor Weon Gyu Shin

Ji Pei is currently an Associate Professor in National Research Center of Pumps, Jiangsu University. His research interests include optimization of pumps, unsteady flow, flow-induced vibration and fluid-structure interaction in fluid machinery. He received his Ph.D. degree from Jiangsu University in 2013.

Wenjie Wang is currently a Research Associate in National Research Center of Pumps, Jiangsu University. His research interests include the optimization design and analysis of the unsteady flow of centrifugal pumps. He received his Ph.D. degree from Jiangsu University in 2017.

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Pei, J., Wang, W., Osman, M.K. et al. Multiparameter optimization for the nonlinear performance improvement of centrifugal pumps using a multilayer neural network. J Mech Sci Technol 33, 2681–2691 (2019). https://doi.org/10.1007/s12206-019-0516-6

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  • DOI: https://doi.org/10.1007/s12206-019-0516-6

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