Prediction of Residual Life of High Temperature and High Pressure Vessels Based on Digital Twin
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.
digital twinspressure vessellife predictionneural network
万方数据
维普
