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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 × 10² before optimization to 7. 744 9 × 10³, and the model is more stable, robust, and fast converging. [ABSTRACT FROM AUTHOR]

Coiled tubing is an important tool in the oil and gas industry, the existence of the actual operation of the running process differs greatly from the results of transmission theory analysis, and in turn, the field operation faces multiple running, causing the safety hazard of coiled tubing. The digital model of coiled tubing was proposed for its running problem, and the contact state algorithm and load analysis physical model of the running process were established, and the synchronous twinning of the whole process of running was carried out to realize the load-bearing state and strength calibration of the tubing column calculated in real time for running coiled tubing, as well as the whole process analysis of the running of coiled tubing. The results show that the contact state of coiled tubing varies with the running depth in real time, and the pipe diameter ratio and friction coefficient are significant factors affecting the self-locking judgment during running. The analysis method proposed in this study can effectively serve for coiled tubing field operations and real-time operational safety assessment, and provide an important basis for coiled tubing service life analysis. [ABSTRACT FROM AUTHOR]