Mechanical Engineering: Fully Funded Swansea and UKAEA PhD Scholarship: AI & Inverse Analysis: Inducing multiple choices in fusion energy designs using AI and inverse analysis (RS105)

Closing date: 5 August 2022

Key Information

Funding providers: Swansea University's Faculty of Science and Engineering and UK Atomic Energy Authority (UKAEA)

Subject areas: Machine learning, computational mechanics, data, digital twin

Project start date: 

  • 1 October 2022 (Enrolment open from mid-September)

Supervisors: 

  • Professor Perumal Nithiarasu (Swansea)
  • Dr Llion Evans (Swansea/UKAEA)
  • Ms Michelle Tindall (UKAEA)

Aligned programme of study: PhD in Mechanical Engineering

Mode of study: Full-time

Project description:

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.

Video- UKAEA and Swansea University collaboration

UKAEA is building various prototypes for fusion components and assemblies. However, due to a range of loading conditions, the complexities involved in designing these make it challenging to create a design space. When following a conventional method, designers are often pinned to an extremely limited design space that may offer only one potential solution. By combining physics informed neural networks (PINNs) with inverse analysis, this project will investigate whether multiple solutions can be induced within a design space. An automated method of providing these multiple solutions to the designer gives materials, topology, and other parameter choices. To implement such a design space, we need to experiment with several different methods. Such outcome-based design will use state-of the-art machine learning methods, but the cost functions will be changed to induce multiple solutions. Also, perturbations and uncertainty in PINNs could be used to introduce multiple solutions. The resulting algorithms will be helpful to build digital twins of fusion systems and provide flexibility to designs. A proposed test case would be on a CHIMERA ‘sample under test’ (SUT), using the available simulation and test data; knowledge of the current design could then be used to explore multiple improved solutions.

The successful candidate will have a good undergraduate degree in a relevant subject, e.g., engineering, physics, or computer science. A postgraduate degree with relevant experience in the topics of this PhD is an added advantage. Previous specialisation in machine learning and/or computational mechanics will allow the student to rapidly start the work. The first year of the PhD will mostly be spent on testing novel machine learning methods for their suitability. The second year will allow the student to move into digital twin design and eventually leading to integration of the model into the workflow at UKAEA in the third year.

The project will be supported by access to high performance computing facilities and funds to cover travel costs.

Eligibility

Candidates 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. See - Country-specific Information for European Applicants 2019 and Country-specific Information for International Applicants 2019.

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 as well has having experience with engineering analysis (e.g. FEA).

English Language requirements: If applicable – IELTS 6.5 overall (with at least 5.5 in each individual component) or Swansea recognised equivalent. Details on the Swansea University English Language entry policy can be found here.

Due to funding restrictions, this scholarship is open to applicants eligible to pay tuition fees at the UK rate only, as defined by UKCISA regulations.

NB: If you are holding a non-UK degree, please see Swansea University degree comparisons to find out if you meet the eligibility. 

If you have any questions regarding your academic or fee eligibility based on the above, please email pgrscholarships@swansea.ac.uk with the web-link to the scholarship(s) you are interested in. 

Funding

This scholarship covers the full cost of UK tuition fees and an annual stipend of £15,609.

Additional research expenses will also be available. 

How to Apply

To apply, please complete your application online with the following information:

  1. Course choice – please select Mechanical Engineering / Ph.D. / Full-time / 3 Year / October

    In the event you have already applied for the above programme previously, the application system may issue a warning notice and prevent application, in this event, please email pgrscholarships@swansea.ac.uk where staff will be happy to assist you in submitting your application.

  2. Start year – please select 2022
  3. Funding (page 8)
  • ‘Are you funding your studies yourself?’ – please select No
  • ‘Name of Individual or organisation providing funds for study’ – please enter ‘RS105 - AI and Inverse Analysis’

*It is the responsibility of the applicant to list the above information accurately when applying, please note that applications received without the above information listed will not be considered for the scholarship award.

One application is required per individual Swansea University led research scholarship award; applications cannot be considered listing multiple Swansea University led research scholarship awards.

As part of your online application, you MUST upload the following documents (please do not send these via email):

  • CV
  • Degree certificates and transcripts (if you are currently studying for a degree, screenshots of your grades to date are sufficient)
  • A cover letter including a ‘Supplementary Personal Statement’ to explain why the position particularly matches your skills and experience and how you choose to develop the project.
  • Two references (academic or previous employer) on headed paper or using the Swansea University reference form. Please note that we are not able to accept references received citing private email accounts, e.g. Hotmail. Referees should cite their employment email address for verification of reference.
  • Evidence of meeting English Language requirement (if applicable).
  • Copy of UK resident visa (if applicable)

For enquiries, please contact Professor Perumal Nithiarasu (P.Nithiarasu@swansea.ac.uk).

*External Partner Application Data Sharing – Please not that as part of the scholarship application selection process, application data sharing may occur with external partners outside of the University, when joint/co- funding of a scholarship project is applicable.