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| Multi-objective optimization design of screw centrifugal pump based on RBF neural network and differential evolution algorithm |
| ZHANG Xiang, HU Beibei, FENG Yiming, LIU Keke, WANG Chunlin* |
| School of Energy and Power engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China |
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Abstract In order to solve the problem of low performance of the spiral centrifugal pump, a typical spiral centrifugal pump was taken as the research object, and the flow field in spiral centrifugal pump was solved by using computational fluid dynamics software CFX.Taking the head and efficiency under the design flow condition as the optimization objective, the optimization variables were screened out by P-B test and multivariate analysis of variance.The radial basis function(RBF)neural network was used to establish the prediction model between the optimization objective and the optimization variables, and the differential evolution(DECIMO)algorithm was used to find the global optimization in the sample space.The optimal head, the optimal efficiency and the initial individual were calculated numerically.The flow field and its characteristics of different medium(clean water and solid-liquid two-phase fluid)were compared and analyzed, and the experimental verification was carried out.The results show that the hub inlet angle β1b, the impeller outlet width b2, the impeller outlet diameter D2 and the blade envelope angle φ are the significant factors affecting the head and efficiency of spiral centrifugal pump. The prediction model established by RBF neural network has high precision. When conveying clean water, the optimal individual head under the design flow is 9.4 m, increasing by 13.5%. The efficiency of the optimal indivi-dual is 9.8% higher than that of the initial individual, and the optimization effect is significant.
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Received: 21 October 2020
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