Predicting the Performance of Helico-Axial Multiphase Pump Using Neural Networks

The main geometric structural parameters which affected the performance of the compression cell of the helico-axial multiphase pump greatly were selected as the research object. The groups of impeller parameters were determined by the orthogonal experimental design method. Then the pressure rise and efficiency for each group which were obtained through numerical simulation according to CFD method were used as the training samples and testing samples in the artificial neural network forecasting process. Two neural network topology structures were determined based on the Back Propagation Neural Network and Radial Basis Function Neural Network respectively. The structure parameters got from the orthogonal design method were used as the input layer data, and the performance parameters from numerical simulation were used as output layer data. After a training progress, two performance prediction models for the helico-axial multiphase pump were established based on the BP and RBF respectively. The testing results showed that the average relative errors for pressure rise and efficiency in the BP network prediction model and were 9.97% and 7.9% respectively, while those in the RBF network prediction model were 7.84% and 5.85% respectively.