Multiparameter optimization for the nonlinear performance improvement of centrifugal pumps using a multilayer neural network
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To increase efficiency at the design point of a centrifugal pump, this study adopted an artificial neural network in the construction of an accurate nonlinear function between the optimization objective and the design variables of impellers. Modified particle swarm optimization was further applied to refine the mathematical model globally. The database, which consisted of 200 sets of impellers, were generated from the Latin hypercube sampling method, and their corresponding efficiencies were obtained automatically from numerical simulation. Design variables were the distributions of blade angles, and results established that the difference between the numerical performance curve and the experimental results was acceptable. Optimization with a two-layer feedforward network improved the pump efficiency at the design point by 0.454 %. Flow complexity improved as the blade curvature increased. The application of the multilayer neural network could provide a meaningful reference to single- and multi-objective optimization of complex and nonlinear pump performance.
KeywordsArtificial neural network Centrifugal pump Nonlinear approximate function Numerical simulation Particle swarm optimization
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This work is supported by the National Key Research and Development Program (Grant No. 2018YFB0606103), National Natural Science Foundation of China (Grant Nos. 51879121, 51779107), China Postdoctoral Science Foundation funded project (Grant No. 2019M651736), Six Talent Peaks Project (GDZB-047) and Qing Lan Project of Jiangsu Province.
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