PhD Studentship: Probabilistic corrosion analysis for secure long-term geological disposal of nuclear waste in high-chloride environments

Lead Supervisor’s full name & email address

Professor Richard Barker - R.J.Barker@leeds.ac.uk

Co-supervisor name(s) & email address(s)

Professor Harvey Thompson - H.M.Thompson@leeds.ac.uk

Dr Joshua Owen - J.J.Owen@leeds.ac.uk

Dr Timothy Hunter - T.N.Hunter@leeds.ac.uk

Project summary

Geological Disposal Facilities (GDFs) are deemed the safest option for nuclear waste disposal, employing a system of natural and engineered barriers for secure storage. Crucial to this process are robust containers manufactured from materials such as stainless or carbon steel, shielded by backfill materials consisting of cement or clay at depths reaching hundreds of meters into rock. Preserving these containers for thousands of years requires a clear understanding of corrosion rates, corrosion product formation, and gas generation, particularly in high-chloride aqueous environments; a vital yet understudied aspect relevant to the UK’s GDF program.

This research programme employs short-term electrochemical and long-term exposure methods to enhance comprehension of material corrosion in hypersaline environments. Reliable test methodologies and statistical analysis techniques will be employed to assure conclusive, quantitative results. Particular emphasis is placed on discerning the impact of pre-closure environments on post-closure corrosion, addressing the potential re-initiation, or continued propagation of existing localised corrosion in high-chloride conditions. The project also capitalises on an array of analysis techniques to understand corrosion product formation and hydrogen evolution. This comprehensive approach mitigates the risk of localised attack on infrastructure materials in GDFs, promoting the longevity and efficacy of nuclear waste disposal systems.

Please state your entry requirements plus any necessary or desired background

Candidates will have, or be due to obtain, a Master’s Degree or equivalent from a reputable university in an appropriate field of Engineering. Exceptional candidates with a First Class Bachelor’s Degree in an appropriate field will also be considered.

Subject Area

Analytical Chemistry, Physical Chemistry, Machine Learning, Mechanical Engineering, Chemical Engineering, Materials Science (Metallurgy), Applied Statistics, Data Analysis

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