The decommissioning of the UK's nuclear legacy sites presents a significant safety challenge: managing hydrogen gas. Generated from nuclear waste through several pathways, hydrogen can accumulate in silos and storage vessels, potentially forming flammable mixtures with air if released. Managing the hydrogen risk on plants requires us to have a much better understanding of how hydrogen accumulates in complex particulate waste streams.
This PhD project will address this urgent challenge by developing a fundamental understanding of how hydrogen gas bubbles are retained and released in granular sediment wastes, of types similar to common ion exchange and filtration media used for effluent treatment. The project will combine state-of-the-art 3D x-ray computer tomography (CT) with enhanced machine learning (ML) simulations of hydrogen gas diffusion flows. Specifically, CT will be used to visualise bubble clusters in waste sediments, allowing quantification of their size and abundance for different hydrogen generation rates. Then, the 3D CT reconstructions will be transferred as input meshes for the simulation of hydrogen transport through these pore spaces, allowing us to model the overall release rates of hydrogen, giving the ability to predict hydrogen transport in these complex operations.
This project offers an opportunity to work at the interface of cutting-edge experimental visualisation techniques and computational modelling, directly impacting the safe and cost-effective decommissioning of sites like Sellafield, Dounreay, and Magnox. You will develop highly sought-after skills in advanced diagnostics, CFD, and nuclear safety, supported by close collaboration with our industrial partner, Sellafield Ltd.