Optimization of pumps as turbines blades based on SVM-HDMR model and PSO algorithm

Abstract

In view of the poor performance of pumps as turbines (PAT) operation, and the problem that the structural parameters cannot be optimized in the whole domain, the hybrid model of support vector machine (SVM) model and high-dimensional model representation (HDMR) method is applied to the optimization of PAT blade. Specifically, a PAT was selected, and the surrogate model for PAT blade optimization was constructed with MATLAB, Creo, and ANSYS software. The particle swarm optimization (PSO) algorithm was used to predict the performance data by global optimization. Finally, numerical prediction and experimental methods were used to verify the predicted data. These proved the applicability of the hybrid model in the optimization of fluid machinery. The numerical simulation results show that at the optimal operating point, the numerical simulation efficiency of the optimized PAT is 5.49% higher than that of the prototype PAT, and the output power is 7.2% higher. The test results show that the external characteristic curve of the numerical simulation PAT is basically consistent with the test results. At the optimal operating point, the test efficiency of the optimized PAT is 5.1% higher than that of the prototype PAT, and the output power is 6.9% higher.