A Digital Twin is a dynamic, bespoke model of a physical system, providing real-time information on the asset instant state and allowing for data extrapolation and predictive modelling.
The application of Digital Twins can open a new chapter in the engineering of particle accelerators and their systems. With Digital Twin, data acquisition and asset modelling can be augmented in space and in time: plentiful additional insight may be provided with respect to ‘standard’ data acquisition of physical parameters, thus allowing for enhanced perspectives in design and engineering; asset prediction is bespoke and real-time, to the great enhancement of predictive and corrective maintenance activities.
This contribution will detail what makes a Digital Twin so unique and different from the historical methods applied in engineering design, data acquisition, monitoring and failure prediction. Applications for accelerator systems will be presented, together with the status of ongoing activities within the Mechanical and Materials Engineering Group at CERN.