Blade pattern optimization of the hydraulic turbine based onneural network and genetic algorithm

A multi-condition optimization method, including the parameterization of blade pattern, the optimization of Latin hypercube experimental design, the CFD techniques, the genetic algorithm-back propagation (GA-BP) neural network and genetic algorithm, was presented for the blade pattern. Specifically, the non-uniform cubic B-spline curve was used to parameterize the blade pattern, and the optimized Latin hypercube experimental design method was employed for the acquirement of the sample points of GA-BP neural network. The performance analysis of each sample point was accomplished by the CFD techniques. Then, the learning and training of the GA-BP neural network was carried out. Finally, the optimization techniques combining the GA-BP neural network and genetic algorithm were used to solve the multi-condition optimization problems of the blade pattern. Based on the above method, the blade pattern of a hydraulic turbine was optimized and improved. The results show that the efficiency of the optimized hydraulic turbine specified in three conditions is increased by 3.91%, 3.59% and 3.09%, respectively, ensuring the constraints of the head are not less than initial head of the hydraulic turbine. This proves that using the above method to optimize the blade pattern is feasible.