Advanced data analysis and machine learning techniques for interpreting AGR reactor data

Primary supervisor

Additional supervisors

  • Gavin Brown

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Other projects with the same supervisor


  • Directly Funded Project (European/UK Students Only)
This research project has funding attached. Funding for this project is available to citizens of a number of European countries (including the UK). In most cases this will include all EU nationals. However full funding may not be available to all applicants and you should read the full department and project details for further information.

Project description

This is a multi-disciplinary project in the fields of machine learning and mechanical modelling with a funded PhD studentship. The student will be based in the School of Computer Science of the University of Manchester, and will closely interact with the EDF Energy R&D UK Centre to understand the industrial context.

The scope of this PhD project is to develop state-of-the-art data analysis and machine learning techniques in order to best interpret the data coming from the models and experiments of the AGR reactors. As they are producing an enormous amount of data, it is necessary to develop appropriate data analysis techniques to extract the relevant information, identify particular crack patterns of interest, and efficiently compare the different runs containing different input data. Machine learning models on finding correlation between inputs of interest (material properties, configuration of cracks, etc...) and outputs of interest (local deformations of the cores, peak forces, etc...) are to be researched.

This project will facilitate The EDF Energy Generation team. They are currently working on the extension of the life expectancy of their Advanced Gas-cooled Reactor (AGR) nuclear power plants. One critical point is the integrity of the graphite cores, which are experiencing significant modifications with ageing due to oxidation, leading to weaker components, and irradiation, leading to greater slackness in the active. Late in the core operating life, graphite components are likely to crack, again leading to greater slackness in the active core and changed external load paths. The team has developed the graphite core models under seismic loading and conducted experiments on a quarter scale simplified mock-up, to increase the understanding of the future behaviour of the AGR graphite cores beyond keyway root cracking, and hence contribute to the underwriting of the safety cases for operation of the cores to their ultimate lifetimes, and enable EDF Energy to take informed lifetime investment decisions.

The University of Manchester is one of the top research-led universities in UK, laying claim to 25 Nobel Prize winners amongst its current and former staff/students, including 4 current Nobel laureates on staff. In REF 2014, the School of Computer Science ranked 4th in the UK by GPA, 1st in overall research environment, with 94% of research classified as world-leading or internationally excellent. The EDF Energy R&D UK Centre is based in the School of Modelling, Aerospace and Civil Engineering of the University of Manchester.

We will consider applicants who have:
* Some existing knowledge in some of the above fields.
* Enthusiasm for research across the above research fields.
* An excellent undergraduate degree in Engineering/Physics, Computer Science or Mathematics (or related discipline), and preferably, a relevant M.Sc. degree.
* Very good experience with computer programming of mathematical models and algorithms (in Python, Matlab or other platforms).
* Excellent report writing and presentation skills.
* Good ability to communicate with fellow students, colleagues and industrial partners.

Please, note that applicants must additionally satisfy the standard requirements for postgraduate studies at the University of Manchester, such as a first-class or high upper-second class (or an equivalent international qualification) and English language qualifications, as stated in the PGR guidelines.

Qualified applicants are strongly encouraged to informally contact the supervising academic Dr. Tingting Mu (, Mr Philippe Martinuzzi ( or Prof. Gavin Brown ( to discuss the application prior to applying.

Person specification

For information


Applicants will be required to evidence the following skills and qualifications.

  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.


Applicants will be required to evidence the following skills and qualifications.

  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.
  • You will have good time management.


Applicants will be required to address the following.

  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Why do you believe you are suitable for doing Postgraduate Research?
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