公开(公告)号：US20240012965A1

公开(公告)日：2024-01-11

申请号：US17920167

申请日：2021-12-27

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Bin YANG ,  Xinyuan ZHANG ,  Ximing SUN ,  Fuxiang QUAN

IPC: G06F30/27 ,  G06F30/28

CPC classification number: G06F30/27 ,  G06F30/28

Abstract: A steady flow prediction method in a plane cascade based on a generative adversarial network is provided. Firstly, CFD simulation experimental data in the plane cascade are preprocessed, and a test dataset and a training dataset are divided from the simulation experimental data. Then, an Encoding-Forecasting network module, a deep convolutional network module and a generative adversarial network prediction model are constructed successively. Finally, prediction is conducted on test set data: the test set data is preprocessed in the same manner, and data dimensions are adjusted according to input requirements of a saved optimal prediction model; and flow field images in the plane cascade at an inlet attack angle of 10° are obtained through the prediction model. The present invention can effectively avoid the problem of limited measurement range of sensors in an axial flow compressor, and the prediction result is highly consistent with the calculation result of CFD.