LAPSE:2019.0811
Published Article
LAPSE:2019.0811
Artificial Neural Networks Approach for a Multi-Objective Cavitation Optimization Design in a Double-Suction Centrifugal Pump
Wenjie Wang, Majeed Koranteng Osman, Ji Pei, Xingcheng Gan, Tingyun Yin
July 28, 2019
Double-suction centrifugal pumps are widely used in industrial and agricultural applications since their flow rate is twice that of single-suction pumps with the same impeller diameter. They usually run for longer, which makes them susceptible to cavitation, putting the downstream components at risk. A fast approach to predicting the Net Positive Suction Head required was applied to perform a multi-objective optimization on the double-suction centrifugal pump. An L32 (84) orthogonal array was designed to evaluate 8 geometrical parameters at 4 levels each. A two-layer feedforward neural network and genetic algorithm was applied to solve the multi-objective problem into pareto solutions. The results were validated by numerical simulation and compared to the original design. The suction performance was improved by 7.26%, 3.9%, 4.5% and 3.8% at flow conditions 0.6Qd, 0.8Qd, 1.0Qd and 1.2Qd respectively. The efficiency increased by 1.53% 1.0Qd and 1.1% at 0.8Qd. The streamline on the blade surface was improved and the vapor volume fraction of the optimized impeller was much smaller than that of the original impeller. This study established a fast approach to cavitation optimization and a parametric database for both hub and shroud blade angles for double suction centrifugal pump optimization design.
Keywords
artificial neural network, cavitation optimization, Computational Fluid Dynamics, multi-objective optimization, NPSHr prediction
Suggested Citation
Wang W, Osman MK, Pei J, Gan X, Yin T. Artificial Neural Networks Approach for a Multi-Objective Cavitation Optimization Design in a Double-Suction Centrifugal Pump. (2019). LAPSE:2019.0811
Author Affiliations
Wang W: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China
Osman MK: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China; Mechanical Engineering Department, Wa Polytechnic, Wa, Upper West, Ghana [ORCID]
Pei J: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China [ORCID]
Gan X: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China
Yin T: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China
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Journal Name
Processes
Volume
7
Issue
5
Article Number
E246
Year
2019
Publication Date
2019-04-27
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr7050246, Publication Type: Journal Article
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LAPSE:2019.0811
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doi:10.3390/pr7050246
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Jul 28, 2019
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Calvin Tsay
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