Detection, measurement and prediction of water flow through grain boundary networks in stainless steel nuclear fuel clad

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 fuel assemblies, which are a stainless steel, 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 then 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 history of the reactor operation.

This project will develop a novel experimental method to image, map and quantify in real time how water locally penetrates the crack networks by modification of the “Ca-test” to detect and quantify moisture permeation, which uses the transparency change of a hydrating calcium coating. To understand how corrosion damage affects this, three-dimensional microstructure characterisation at selected sites (X-ray computed tomography and electron microscopy) will be correlated directly with the locally measured water penetration.

Modelling, such as 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 simulate the effects of the multi-dimensional connectivity of these networks. Going beyond the limitations of conventional fluid flow models, this aims to enable simulations at large scale of the transport properties of damaged microstructures. Statistical (‘Monte-Carlo’) models may then 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.