Data-driven computational modelling of fracture of nuclear graphite bricks

Cracks in the nuclear graphite core of the UK's advanced gas-cooled reactors are the result of long-term exposure to the reactor's aggressive environment, and degradation of material properties. The University of Glasgow has developed a unique physics-based modelling capability for predicting this unstable crack propagation. This will be used by EDF Energy for future safety cases. Using machine learning (ML) techniques, statistical analysis and graph theory the student will further improve these physics-based models from data obtained from the real operating environment. Such techniques will also enable the objective selection of bricks to undertake further analysis, which a key source of uncertainties. It will also be possible to classify bricks in terms of cracks morphologies. Similarly, operational data will be supplemented with data from the physics-based models for improved decision making in life extension. ML will also enable uncertainty in model parameters to be propagated, and their influence explored.