Data-driven validation framework for thermal modelling of plasma facing components – materials strand project

Supervisors: Dr Ksenija Dvurecenska (University of Liverpool), Dr Adel Tayeb (UKAEA)

In a nuclear fusion reactor Plasma Facing Components (PFCs) are subjected to extreme environmental conditions which induce thermomechanical loads. To build confidence in the thermal performance and integrity of PFCs and consequently a robust operation of a reactor, we must test individual components under the representative loads. It is 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, e.g. engineering simulations. However, simulations require validation against experimental data over domains that can be physically tested to ensure that they are credible and can be used for risk informed decision making. In this project the student will aim to link the two testing environments by developing a data-driven validation framework, which draws on data from multiple sensors and demonstrates credibility of an engineering simulation.

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 multiphysics 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, multiphysics 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, e.g. paper reviews, presentations and lab demonstrations.

The project will be mainly based in Liverpool, but will require long stays at the Fusion Technology Facility in Yorkshire (FTF-Y) UKAEA for collaboration with their data acquisition and modelling specialists. Opportunities will be sought for attending conferences to disseminate findings and networking opportunities for the student.

This project may be compatible with part time study, please contact the project supervisors if you are interested in exploring this.

This project is offered by University of Liverpool. For further information please contact: Dr Ksenija Dvurecenska,