Fluid Analysis and Structure Optimization of Impeller Based on Surrogate Model

Abstract

The surrogate model technology has a good performance in solving black-box optimization problems, which is widely used in multi-domain engineering optimization problems. The adaptive surrogate model is the mainstream research direction of surrogate model technology, which can realize model fitting and global optimization of engineering problems by infilling criteria. Based on the idea of the adaptive surrogate model, this paper proposes an efficient global optimization algorithm based on the local remodeling method (EGO-LR), which aims at improving the accuracy and optimization efficiency of the model. The proposed algorithm firstly constructs the expectation improvement (EI) function in the local area and optimizes it to get the update points. Secondly, the obtained update points are added to the global region until the global accuracy of the model meets the requirements. Then the differential evolution algorithm is used for global optimization. Sixteen benchmark functions are used to compare the EGO-LR algorithm with the existing algorithms. The results show that the EGO-LR algorithm can quickly converge to the accuracy requirements of the model and find the optimal value efficiently when facing complex problems with many local extrema and large variable spaces. The proposed algorithm is applied to the optimization design of the structural parameter of the impeller, and the outflow field analysis of the impeller is realized through finite element analysis. The optimization with the maximum fluid pressure (MP value) of the impeller as the objective function is completed, which effectively reduces the pressure value of the impeller under load.