Detection, measurement and prediction of water flow through grain boundary networks

Safe, secure storage of spent fuel from the UK fleet of advanced gas-cooled nuclear reactors (AGR) must be assured until a geological disposal facility is operational. The spent oxide fuel assemblies are stored in water as they require cooling from the heat generated by radioactive decay. The corrosion resistance of certain grain boundaries in their steel may be reduced (https://doi.org/10.1016/j.corsci.2014.04.050, 

https://doi.org/10.1016/j.matdes.2019.108368) and corrosion damage through the cladding may allow water to percolate to the fuel. This creates a risk of fission product release. The potential damage depends on the steel chemistry, its thermomechanical processing and the operation of the reactor.

This project will modify the “Ca-test”, which uses the transparency change of a hydrating calcium coating to detect and quantify moisture permeation, to develop a novel method to image, map and quantify in real time how water locally penetrates. To understand how corrosion damage affects this, three-dimensional microstructure characterisation at selected sites will be correlated directly with the observed local water penetration. A computationally efficient topological approach (https://doi.org/10.1180/minmag.2012.076.8.12, https://doi.org/10.1016/j.tafmec.2007.08.007) will be applied to model the multi-dimensional connectivity of these networks. Going beyond the limitations of conventional fluid flow models, this will allow enable simulations at large scale of the transport properties of different damaged microstructures. The aim is to use statistical (‘Monte-Carlo’) models to predict the risk of fission product release through the damage networks that might develop with long term storage. The project is suitable for graduates with an engineering, mathematical or physical sciences background.