Abstract:Aiming at the technical problem that the flow state of radial diffuser multistage pump is complex and changeable, which makes it difficult to accurately measure its hydraulic performance curve, this paper adopted genetic algorithm to optimize the weight and threshold of BP neural network. We constructed a hydraulic performance prediction model of radial diffuser multistage pump based on GA-BP neural network. Based on MD500-57 as the research object, we established GA-BP neural network of the input layer to 13 neurons, hidden layer to 10 neurons, and output layer to 2 neurons. The test scheme designed by using the orthogonal test method was solved by numerical simulation method. The GA-BP neural network was trained and tested based on training samples. Then, we calculated the optimal combination scheme of the key geometric parameters in the overcurrent component. The tests show that the head of the radial diffuser multistage pump is increased by 2.4m, the efficiency is increased by 3.34%, and the range of high efficiency zone is widened