In a nuclear fusion reactor Plasma Facing Components (PFCs) are subjected to extreme environmental conditions which induce complex loads. To build confidence in the integrity of PFCs and consequently a robust operation of a reactor, we must test individual components under the complex multi-physics loads representative of the operating conditions. It’s expensive, time consuming and difficult to physically test the components under the extreme environmental conditions, making it desirable to enable the use of virtual tests, eg engineering simulations, to qualify these components. However, simulations require validation against experimental data over domains that can be physically tested to ensure that they’re credible and can be used for risk informed decision making. In this project, the successful student will aim to integrate physical and virtual testing to develop a multi-physics data-driven validation framework, which synthesises data from multiple sensors and demonstrates credibility of an engineering simulation. This project will focus on component qualification in fusion; however, the novel framework will have the potential to extended to other engineering applications based on multi-physics systems.
To validate performance of Plasma Facing Components new approaches are vital. The breadth of information collated from multiple sensors and measurement techniques must be combined and fully utilised during the validation process, otherwise it can lead to a limited understanding of engineering simulations based on computational multi-physics models and hinder their credibility. The student will investigate and develop new methods to synthesize data from different sources into a single framework that will be used to qualify fusion components. The outcome of this project will increase the value of the data generated by physical tests by creating a systematic method for connecting the data to simulations. Overall, the knowledge and tools developed in this project will contribute to successful delivery of clean energy based on fusion.
The successful student will develop practical and analytical skills in experimental mechanics, multi-physics simulations and machine learning, which are highly desirable in the industry, and thus will strengthen the student’s employability post PhD. They will work closely with experts at UKAEA and become part of a dynamic research group at the School of Engineering, University of Liverpool. The student will participate in regular research group meetings, which include a variety of activities to develop additional transferable skills, eg paper reviews, presentations and lab demonstrations.
The studentship is part of the EPSRC Centre for Doctoral Training in Fusion Power. The student will get to interact with a cohort of students at six leading UK universities.