Abstract：Based on the theory of boundary vorticity dynamics, the force and moment of an arbitrary shape object can be obtained by the boundary vorticity flux integral. Therefore, if the distribution of the boundary vorticity flow on the inner surface of the centrifugal pump impeller can be predicted, the internal flow condition and force of the impeller can be analyzed and known, which helps to improve the design of the impeller. Hence, the BP neural network and radial basis neural network were chosen as the modeling methods, and the boundary vorticity flux on the inner surface of impeller was taken as the prediction target. Firstly, high-accuracy CFD calculations were performed to obtain the BVF distributions in 70 centrifugal pump impellers and construct 70 groups of initial training samples. Then, 63 initial samples were used to establish the nonlinear mapping relation between the geometric parameters of the centrifugal pump impeller and the boundary vorticity flux. Besides, the remaining 7 proofread samples were used to test the relation by comparing the predicted values of the neural networks with the calculated values of numerical simulations. According to the magnitude of the error, the predictive performance of the artificial neural network was evaluated. It shows that compared with BP neural network, RBF neural network has higher prediction accuracy, shorter training time and higher opera-tion stability. The width of radial basis function has a great influence on the prediction performance of RBF neural network. When the radial basis function width is set as 0.3, the RBF neural network has the best prediction performance and the prediction error is only 0.020 3. The boundary vorticity flux distribution on the inner surface of the impeller predicted by the RBF neural network can be used as an important index to evaluate the hydraulic design of the impeller, and then guide the optimal design of the turbomachinery.
