Risk CDT – Realistic model prediction for managing risk in nuclear decommissioning

PLEASE APPLY ONLINE TO THE SCHOOL OF ENGINEERING, PROVIDING THE PROJECT TITLE, NAME OF THE PRIMARY SUPERVISOR AND SELECT THE PROGRAMME CODE "EGPR" (PHD - SCHOOL OF ENGINEERING)

This is a project within the multi-disciplinary EPSRC and ESRC Centre for Doctoral Training (CDT) on Quantification and Management of Risk & Uncertainty in Complex Systems & Environments, within the Institute for Risk and Uncertainty. The studentship is granted for 4 years and includes, in the first year, a Master in Decision Making under Risk & Uncertainty. The project includes extensive collaboration with prime industry to build an optimal basis for employability.

Decommissioning of legacy nuclear facilities from decades old military and civil programmes represents a significant challenge to the UK. Due to the fact that uncertainties are not underpinning, the potential to optimise decommissioning strategies could save the UK tax payer many £100’s millions of pounds over the life time of decommissioning of a single large facility, such as the MAGNOX reprocessing facilities at Sellafield.

Determining the optimal strategy for nuclear decommissioning is dependent on generating realistic data to represent future scenarios. This is a particular problem in effluent management on nuclear sites where historic data representing the effluent challenge generated during normal operations under largely steady state conditions provides a poor representation of challenges that will occur during decommissioning. Effluent feeds consist of radioactive ions that must be abated and competing ions that affect the abatement performance. The problem is exacerbated by the fact that abatement is understood to respond in a non-linear fashion to stochastic variation of the feed challenge. The challenge is to identify methods to generate artificial data that could reflect changes brought upon by decommissioning but retain the inherent variability that is currently observed. This would require the development of methods to establish the credibility of this data by determining its validity and veracity. Establishing credibility of the approach is a key step in ensuring that the methods can be adopted within decommissioning programmes to realise the benefits as outlined.

The applicant should have a very strong mathematical background, computational skills, and
knowledge in probability theory and mathematical statistics.