A Flow Information-Based Prediction Model Applied to the Nonaxisymmetric Hub Optimization of a Centrifugal Impeller

Abstract

A full-range prediction model for turbomachinery based on the flow-field information code is established in this article to solve the problems that traditional models do not have enough prediction accuracy and cannot reflect the complete performance characteristics of the impeller. The model, which can predict the complete performance curve of the impeller with higher accuracy, consists of two multilayer artificial neural network (ANN) submodels. Different from the traditional model, the ANN submodel uses the flow-field information code for pretraining layer by layer. The flow-field information code is the characteristic information extracted from the impeller flow field through the proper orthogonal decomposition (POD) method. By implicitly learning the flow-field information, the prediction error of the model is reduced by 29.7% compared with the single hidden layer ANN. Based on this model, the nonaxisymmetric, but periodic, hub optimization of a centrifugal impeller with 30 variables is carried out, with the goals of the higher efficiency and the wider flow range at the specified pressure ratio and the massflow rate at the design point. The result shows that, after the optimization, the isentropic efficiency at the design point increases by 1% and the flow range increases by 2% compared to the baseline.