基于卷积神经网络双层壁三维热应力预测方法

To realize the prediction of 3D physical field， a method for rapid evaluation of 3D thermal stress on the outer wall of double-wall cooling structure using convolutional neural network (CNN) was presented. For the structural characteristics of the flat plate shape of the double-wall cooling structure outer wall， the temperature field was sliced into multiple sections along the wall thickness direction. The temperature was used as the basic element of the input tensor of the CNN，and the sections at different thickness positions corresponded to channel dimensions of the input tensor. The 3D temperature field was input into the CNN for output of the 3D von-Mises stress field under thermal load. The average absolute error of the trained converged network on the testing set was 1.23 MPa， with an average relative error of 15.10%，and the average absolute error for peak stresses was 16.10 MPa，with an average relative error of 11.81%. Results showed that for the thermal stress prediction problem of double-wall cooling structures， CNN can complete the temperature-to-stress mapping well. Exploring potential mechanisms of the thermoelasticity problems by using deep learning methods is expected to be realized.