Diffusion of corrosion agents through an engineered barrier system

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Many countries are studying long-term solutions for safe storage of used nuclear fuel. One such solution,**selected by the Canadian Nuclear Waste Management Organization (NWMO), is the use of deep geological**repositories (DGRs) that would be located several hundred metres below ground level. DGRs would store used**fuel canisters (UFCs) surrounded by an engineered barrier system (EBS), composed of copper coatings to act as**a corrosion barrier and highly compacted bentonite (HCB) that surrounds the UFC. HCB swells upon contact**with water and can therefore seal gaps within the repository and provide a moderately thermally conductive**medium. It can also slow groundwater infiltration and retard the movement of radionuclides in the event of a**UFC breach. In addition, the HCB can suppress microbial activity near the container by restricting space and**water availability to microbes that may be present. However, diffusion of sulphide can result in microbially**influenced corrosion (MIC), potentially causing early failure of the barrier. The proposed research will**investigate the factors that can affect sulfide diffusion to the UFC surface. The project will include diffusion**experiments performed under a range of potential repository conditions, based on current Canadian design**specifications and predictions for DGR conditions. The results of the experimental studies will be used to**further expand and validate a three-dimensional computer model. The resulting model will be capable of**parametric studies to refine and optimize the design of the Canadian DGR. The results of this study will**identify key parameters that may result in higher corrosion rates, leading to a refinement of NWMO's EBS**design and providing Canadians with a safe and sustainable method of used nuclear fuel disposal. This study**will address significant scientific questions regarding diffusion of corrosive species through the engineered**barrier system in sub-surface environments, and will also enhance predictive modelling capabilities to**strengthen the sponsoring agency's long-term DGR design/performance criteria.