Digital Twin framework development for SPARC and ARC

The mission of the SPARC tokamak is to close critical science and technology gaps needed to design and operate the ARC power plant, enabled by quickly reaching the maximum projected performance of many SPARC systems. This goal is likely to be accelerated if SPARC can deploy a digital twin that can be updated rapidly in parallel with operations, quantify the machine state with respect to operational budgets and component lifetime, and fold in new data as operational space is expanded. Demonstrating the value of a digital twin on SPARC would also lay the groundwork for use on ARCs where digital twins can aid in constraining machine state awareness for a very sparse diagnostic set and can learn from a fleet of power plants. To build such a framework will require unifying physics and engineering models across a wide range of spatial and temporal scales, and it will require harmonization of many different types of computational algorithms into a cohesive framework.

In this work we outline the broader context in which CFS sees the potential for digital twins to make an impact on fusion energy, and highlight the challenges that a SPARC digital twin framework could address, using one of the most challenging key issues for a next step device, power exhaust, as an example.