Knowledge Mining of Low Specific Speed Centrifugal Pump Impeller Based on Proper Orthogonal Decomposition Method
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https://doi.org/10.1007/s11630-020-1356-5
Article ID: 1003-2169(2020)00-0000-00
Knowledge Mining of Low Specific Speed Centrifugal Pump Impeller Based on Proper Orthogonal Decomposition Method ZHANG Renhui1*, CHEN Xuebing1, LUO Jiaqi2 1. School of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China 2. School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China © Science Press, Institute of Engineering Thermophysics, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract: To clarify the complex relation between the pump blade shape and its corresponding hydraulic performance, the knowledge mining method of centrifugal pump impeller based on proper orthogonal decomposition (POD) was proposed. The pump blade shape was parameterized by cubic Bezier curve. The Latin hypercube design method was employed to supply the necessary samples for producing the perturbations of blade wrap angle, and blade angle at inlet and outlet. The hydraulic efficiency and head were optimized by NSGA-II and RBF hybrid algorithm. The Pareto-optimal solutions were obtained. In order to further illustrate the relationship between the centrifugal pump blade shape and its hydraulic performance, the POD method was used to discover the effects of optimized blade shape to the flow solutions. For the optimization of centrifugal pump MH48-12.5, blade shape and relative velocity field in impeller from Pareto-optimal solutions were analyzed. The results demonstrate that larger blade angle and smaller wrap angle increase the average kinetic energy in impeller, resulting in higher pump head design. Smaller blade angle and larger wrap angle decrease the velocity gradient from the pressure side to suction side, resulting in smaller hydraulic loss and higher efficiency design.
Keywords: centrifugal pump, multi-objective optimization, proper orthogonal decomposition, knowledge mining
1. Introduction The hydraulic optimization design of centrifugal pump is essentially the flow optimization problem. However, the complexity of inner flow and the complex implicit relationship between inner flow and hydraulic performance lead to slow progress in pump optimization design [1–3]. At present, the optimization methods of fluid machinery mainly include gradient optimization method, approximation model method, evolutionary algorithm, etc. For the gradient optimization method [4–6], the main calculation amount is to evaluate the gradient vector of objective function to design variables. As the dimension of design variable increases, the Received: Aug 31, 2019
AE: ZHANG Chuhua
calculation amount increases exponentially, and this would probably cause the curse of dimensionality. The approximate model methods include radial basis function (RBF) model, Kriging model, artificial neural network model, etc. [7–9]. The approximate model can find the global optimized design quickly by constructing the approximate response relationship between objective function and control variable. The optimization
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