Fast reconstruction method of the stress field for the steam turbine rotor based on deep fully convolutional network

Since it is difficult to directly measure the transient stress of a steam turbine rotor in operation, a rotor stress field reconstruction model based on deep fully convolutional network for the start-up process is proposed. The stress distribution in the rotor can be directly predicted based on the temperature of a few measurement points. First, the finite element model is used to accurately simulate the temperature and stress field of the rotor start-up process, generating training data for the deep learning method. Next, data of only 15 temperature measurement points are arranged to predict the stress distribution in critical area of the rotor surface, with the accuracy (R2-score) reaching 0.997. The time cost of the trained neural network model at a single case is 1.42s in CPUs and 0.11s in GPUs, shortened by 97.3% and 99.8% with comparison to finite element analysis, respectively. In addition, the influence of the number of temperature measurement points and the training size are discussed, verifying the stability of the model. With the advantages of fast calculation, high accuracy and strong stability, the fast reconstruction model can effectively realize the stress prediction during start-up processes, resulting in the possibility of real-Time diagnosis of rotor strength in operation.