Digital Twin for Predicting Progressive Damage in Operating Pressure Vessels

Abstract

According to the definition of the French Alliance “Industry of the Future”, the digital twin is a virtual clone of a physical system or a process. It systematically implies the existence of a "digital model" coupled with the object it copies. Depending on the system concerned and the desired usage, it can be a geometric, multiphysical, functional, behavioral, and decision-making model. It must evolve over time like its real twin. It can be used to improve the control, security and optimization of production lines and factories, digital continuity at the product level, from its design to its end of life, monitoring and predictive maintenance.The digital twin appears as a reliable way to monitor operation, to evaluate the resistance and safety of pressure vessels (PVs) in real service conditions. Via the connected transducers (IoT), we can integration the life cycle information in an automatic data processing module, and finally to capitalize on all this data to optimize the design of new products.To this end, the first objective of the thesis is to contribute to the development of a methodology for real-time assessment of the integrity of pressure vessels, by implementing a predictive maintenance strategy in place of the classical and costly, preventive or curative maintenance, based on IoT and digital twin technologies. The second technological goal of the thesis is to develop methods to optimize and make reliable the design and operation of new generations of pressure structures.