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.