Automatic optimization of axial flow pump based on radial basis functions neural network and CFD
LU Rong1, YUAN Jianping1, LI Yanjun1, JIANG Hongying2
1.National Research Center of Pumps, Jiangsu University, Zhenjiang, Jiangsu 212013, China; 2.Jiangsu Province Water Engineering Sci-tech Consulting Co. Ltd., Nanjing, Jiangsu 210000, China
Abstract：Parametric design and computational fluid dynamics(CFD)techniques have been widely applied to the optimum design of fluid machinery. In this paper, the axial flow pump was designed by turbomachinery design software-CFturbo, which was integrated with PumpLinx by batch commands based on Isight software to realize the CFD automatic optimization of axial flow pump. Seven design variables of the blade profile and diffuser were selected through Optimal Latin Hypercube Design(Opt LHD)to generate design points within the selected design space. The hydraulic efficiency at a designed flow rate was set as the objective function. A surrogate model, Radial Basis Functions(RBF)neural network was constructed for the objective function based on the numerical results. Finally, the best combination of the design variables was obtained by solving the approximation model with the Multi-Island Genetic Algorithm(MIGA). The results show that the performance curve simulated by CFD had a good agreement with that of the experiment, thus the RBF model can predict the efficiency accurately. After optimization, the efficiency was improved by 4.46%, while the head kept almost constant under the design condition. Through the Pareto Graph analysis, the significance levels of design variables were obtained, which can provide a certain reference for optimum design of the axial flow pump.
陆荣,袁建平,李彦军,蒋红樱. 基于神经网络模型和CFD的轴流泵自动优化[J]. 排灌机械工程学报, 2017, 35(6): 481-487.
LU Rong, YUAN Jianping, LI Yanjun, JIANG Hongying. Automatic optimization of axial flow pump based on radial basis functions neural network and CFD. Journal of Drainage and Irrigation Machinery Engin, 2017, 35(6): 481-487.
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