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Swarm intelligence based on modified PSO algorithm for the optimization of axial-flow pump impeller

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Abstract

This paper presents a multi-objective optimization of the impeller shape of an axial-flow pump based on the Modified particle swarm optimization (MPSO) algorithm. At first, an impeller shape was designed and used as a reference in the optimization process then NPSHr and η of the axial flow pump were numerically investigated by using the commercial software ANSYS with the design variables concerning hub angle βh, chord angle βc, cascade solidity of chord σc and maximum thickness of blade H. By using the Group method of data handling (GMDH) type neural networks in commercial software DTREG, the corresponding polynomial representation for NPSHr and η with respect to the design variables were obtained. A benchmark test was employed to evaluate the performance of the MPSO algorithm in comparison with other particle swarm algorithms. Later the MPSO approach was used for Pareto based optimization. Finally, the MPSO optimization result and CFD simulation result were compared in a re-evaluation process. By using swarm intelligence based on the modified PSO algorithm, better performance pump with higher efficiency and lower NPSHr could be obtained. This novel algorithm was successfully applied for the optimization of axial-flow pump impeller shape design.

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Authors and Affiliations

  1. School of Mechanical Engineering, Pusan National University, Pusan, 609-735, Korea

    Fuqing Miao, Cholmin Kim & Seokyoung Ahn

  2. School of Mechanical and Automotive Engineering, Ulsan University, Ulsan, 680-749, Korea

    Fuqing Miao & Hong-Seok Park

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  1. Fuqing Miao
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  2. Hong-Seok Park
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  3. Cholmin Kim
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  4. Seokyoung Ahn
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Corresponding author

Correspondence to Seokyoung Ahn.

Additional information

Recommended by Editor Haedo Jeong

Fuqing Miao received his bachelor degree in Central South University and master degree in University of Ulsan. He is currently a Ph.D. candidate student at Pusan National University, majoring in Precision Processing System at the College of Mechanical Engineering. His research interests are multi-objective optimization, PSO algorithm, fluid mechanics and their applications.

Seokyoung Ahn received his BS degree in Mechanical Engineering from Pusan National University, M.S. degree from POSTECH and Ph.D. degree from the University of Texas at Austin. His research interests are modeling, estimation and control of nonlinear manufacturing process such as Electroslag remelting process. His other research area includes nuclear site decommissioning technologies such as melt decontamination.

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Miao, F., Park, HS., Kim, C. et al. Swarm intelligence based on modified PSO algorithm for the optimization of axial-flow pump impeller. J Mech Sci Technol 29, 4867–4876 (2015). https://doi.org/10.1007/s12206-015-1034-9

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  • DOI: https://doi.org/10.1007/s12206-015-1034-9

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