In order to solve the difficult problem of online prediction of the remaining life of high-temperature and high-pressure vessel,a method of constructing the remaining life prediction model of high-temperature and high-pressure vessel based on digital twin was proposed.The method was based on real-time working conditions,using ANSYS simulation model for coupled simulation,obtaining a certain time-domain physical field of high-temperature and high-pressure vessel,establishing a sample dataset of remaining life prediction of high-temperature and high-pressure vessel through the multiaxial creep damage model,and using BP(back propagation)neural network algorithm optimized by Tent-SSA for training prediction,to establish a digital twin high-temperature and high-pressure vessel life prediction model driven by the fusion of the mechanism model and machine learning.Finally,the tube plate,which is a key component of a certain sodium-cooled fast reactor steam generator,was used as an object,and the experimental results show that the overall mean square error of the prediction model is reduced from 3.219 7 x 10-2 before optimization to 7.744 9 x 10-3,and the model is more stable,robust,and fast converging.
关键词
数字孪生/压力容器/寿命预测/神经网络
Key words
digital twins/pressure vessel/life prediction/neural network
维普
万方数据