| description abstract | A computer three-dimensional (3D) vision-aided performance prediction framework for turbomachinery is established in this paper, to improve the accuracy and generalization ability of the artificial neural network (ANN) model under inputs of more than 90 control parameters. In this framework, a RandLA-encoder is built to extract the flow information related to performance and geometric parameters from point cloud data of flow fields inside impellers. By implicitly learning this kind of flow information, the prediction error of the ANN model is reduced by 20–30% compared with the traditional one. Based on this, a full-3D optimization with 91 variables, including arbitrary blade surface and non-axisymmetric (but periodic) hub surface, is conducted on Krain low-speed impeller, aiming at a comprehensive performance improvement. After the optimization, compared to the baseline, the maximum isentropic efficiency of the compressor is increased by 1.6%, the isentropic efficiency at design point is increased by 1%, and the flow range is increased by 5%, with a slight increase in pressure ratio. | |
