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Hydraulic optimization of a double-channel pump’s impeller based on multi-objective genetic algorithm

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Abstract

Computational fluid dynamics (CFD) can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to improve such hydrodynamic performance. In this paper, a more convenient and effective approach is proposed by combined using of CFD, multi-objective genetic algorithm (MOGA) and artificial neural networks (ANN) for a double-channel pump’s impeller, with maximum head and efficiency set as optimization objectives, four key geometrical parameters including inlet diameter, outlet diameter, exit width and midline wrap angle chosen as optimization parameters. Firstly, a multi-fidelity fitness assignment system in which fitness of impellers serving as training and comparison samples for ANN is evaluated by CFD, meanwhile fitness of impellers generated by MOGA is evaluated by ANN, is established and dramatically reduces the computational expense. Then, a modified MOGA optimization process, in which selection is performed independently in two sub-populations according to two optimization objectives, crossover and mutation is performed afterword in the merged population, is developed to ensure the global optimal solution to be found. Finally, Pareto optimal frontier is found after 500 steps of iterations, and two optimal design schemes are chosen according to the design requirements. The preliminary and optimal design schemes are compared, and the comparing results show that hydraulic performances of both pumps 1 and 2 are improved, with the head and efficiency of pump 1 increased by 5.7% and 5.2%, respectively in the design working conditions, meanwhile shaft power decreased in all working conditions, the head and efficiency of pump 2 increased by 11.7% and 5.9%, respectively while shaft power increased by 5.5%. Inner flow field analyses also show that the backflow phenomenon significantly diminishes at the entrance of the optimal impellers 1 and 2, both the area of vortex and intensity of vortex decreases in the whole flow channel. This paper provides a promising tool to solve the hydraulic optimization problem of pumps’ impellers.

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Authors and Affiliations

  1. School of Energy and Power Engineering, Jiangsu University, Zhenjiang, 212013, China

    Binjuan Zhao, Yu Wang, Huilong Chen & Jing Qiu

  2. Shanghai Kaiquan Pump (Goup) Co., Ltd, Shanghai, 201804, China

    Duohua Hou

Authors
  1. Binjuan Zhao
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  2. Yu Wang
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  3. Huilong Chen
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  4. Jing Qiu
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  5. Duohua Hou
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Corresponding author

Correspondence to Binjuan Zhao.

Additional information

Supported by National Natural Science Foundation of China(Grant No. 51109094), and Priority Academic Program Development of Jiangsu Higher Education Institutions of China

ZHAO Binjuan, born in 1977, is currently an associate professor at School of Energy and Power Engineering, Jiangsu University, China. She received her PhD degree from Jiangsu University, China, in 2007. Her research interests include numerical simulation of multi-phase flow in fluid machinery and optimization design of fluid machinery.

WANG Yu, born in 1989, is currently a master candidate at School of Energy and Power Engineering, Jiangsu University, China. He received his bachelor degree from Jiangsu University, China, in 2012.

CHEN Huilong, born in 1961, is currently a professor at School of Energy and Power Engineering, Jiangsu University, China. He received his PhD degree from Jiangsu University, China, in 2007.

QIU Jing, born in 1990, is currently a master candidate at School of Energy and Power Engineering, Jiangsu University, China. He received his bachelor degree from Jiangsu University, China, in 2013.

HOU Duohua, born in 1988, is currently a CFD engineer in Hydraulic Study Department of Shanghai Kaiquan Pump (Goup) Co., Ltd, China. She received her master degree from Jiangsu University, China, in 2014.

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Zhao, B., Wang, Y., Chen, H. et al. Hydraulic optimization of a double-channel pump’s impeller based on multi-objective genetic algorithm. Chin. J. Mech. Eng. 28, 634–640 (2015). https://doi.org/10.3901/CJME.2015.0116.016

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  • DOI: https://doi.org/10.3901/CJME.2015.0116.016

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