Digital Twin Model Development for Through-Life Management of the Divertor in a Fusion Power Plant

I completed my undergraduate degree in Physics at the University of York in 2017. Afterwards, I decided I wanted to work with people with complex needs such as brain injuries and found this incredibly rewarding. In 2018, I started my career in data roles, first in a clinical trials testing laboratory and then at a medical and temperature-controlled packaging company. In 2020, I worked at a medical imaging software start-up in Oxford where I helped build deep learning neural network models to delineate structures within CT and MR scans. From 2022, I have worked as a data scientist within a nuclear engineering industry, making this transition because I have always been interested in fusion energy since university. This led me towards the Fusion CDT after I found a project which fit both my data skills and physics knowledge.
Divertors are a key aspect within MCF, especially in spherical tokamaks using a H-mode plasma. The divertor is able to handle plasma effects beyond the X-point via exhausting impurities and removing heat. It will therefore encapsulate an area of the tokamak in which key diagnostics can be found. However, currently, the divertor is a very complex component to design, model and control. Therefore, there is a need to develop a digital twin of the divertor to capture models and data to provide a more complete picture of the reactor. This will help understand the state of the materials and components, alert users to predictive maintenance and run what-if scenarios using models of the plasma and plasma-facing materials. A key aspect of the hybrid modelling approach will be quantitative validation of multi-physics and data-driven models and establishing credibility in failure predictions through physical testing and quantifying uncertainty. These will be very useful skills for future design, manufacturing, implementation and through-life management of future fusion energy plants.
This PhD project is being co-funded by Assystem, a global nuclear engineering company with specialities in engineering, digital and project management. Assystem has knowledge and experience in both nuclear fission and fusion such as supporting UKAEA's fusion experiments in Culham and in its role as architect engineer for ITER.