Fully funded EPSRC DTP PhD scholarship: Using machine learning to maximise impact of fusion energy experimental facility virtual twin

Applications are invited for a fully funded EPSRC DTP PhD scholarship in Engineering and Computer Science.

The inside of a fusion reactor is one of the most challenging environments known about, with temperatures ranging from the hottest in the solar system (100,000,000 °C at the centre of the plasma) to the coolest (-269 °C in the cryopump) all within a few metres, coupled with electro-magnetic loads and irradiation damage. This has already been achieved for short periods of time at JET, the world’s largest fusion device located at Culham Centre for Fusion Energy (UKAEA), UK. But one of the greatest engineering challenges of the 21st century will be to construct a machine that can operate under these extremes routinely and produce commercially viable energy.

The ’Heat by Induction to Verify Extremes’ (HIVE) facility at UKAEA is used to test components under the extreme environments experienced in a fusion device. To maximise the value of experimental data the candidate will work as part of a team (involving partners from academia, our national institutes (e.g. STFC) and industrial partners) to build Digital Twin capability around the UKAEA HIVE Facility. Each time an experiment is performed the developed platform will use the input parameters to auto-trigger an equivalent simulation running the twin through the same duty cycle as the real apparatus. A scheme will be developed to auto-compare experiment and simulation to warn if the equipment needs recalibrating or if large discrepancies are present, and whether there is confidence in the experiment. The scheme can alert that the model is not accurately capturing all the mechanisms of the testing regimes.

A machine learning framework will then be developed to query the database of experiment/simulation results built up over time. The aim would be to use this for steering which future tests to perform (reducing the number required), optimising experimental run time (again, by identifying the optimal parameter settings), anomaly detection and predictive maintenance. Ultimately the goal is to turn a much larger fraction of the data recorded into invaluable information. We are now in an era where all previous experiments should form a “prior” for future experiments and operational planning – no longer does it make sense to employ “experts” and to rely upon their memories, experience and gut feeling in the way we do our experiments and model verification.

This project will provide the opportunity to develop transferable skills in industrial processes; component qualification; high-performance computing and computer programming - thus ensuring the candidate is prepared for a wide range of possible career paths after graduation.

Location: Zienkiewicz Centre for Computational Engineering, Bay Campus, Swansea University

Supervisors: Dr Llion Evans (Swansea/UKAEA: http://www.swansea.ac.uk/staff/engineering/llion.evans), Prof Perumal Nithiarasu (Swansea: http://www.swansea.ac.uk/staff/engineering/p.nithiarasu/), Dr Rob Akers (UKAEA)

Eligibility
Applicants should hold a first or upper second class honours degree (or its equivalent) in engineering, computer science, mathematics, or physics, or a master’s degree in a subject area related to the project.

A strong background in numerical methods or machine learning is required. Knowledge/experience of programming in compiled languages (e.g. C, C++, or Fortran) and interpreted languages (e.g. Python) is essential and CUDA is desirable. Previous specialisation in Fusion Energy is not required as the student will gain a broad understanding of the key concepts behind Fusion Energy through working closely with the industrial sponsor UKAEA (https://www.youtube.com/watch?v=Q3Yjys3QHuk).

Due to funding restrictions, this scholarship is open to UK/EU candidates only. For more information, please visit: www.epsrc.ac.uk/skills/students/help/eligibility