Development of CAN2 (Canister Corrosion Analysis, Assessment and ActioN PlaNs), a Predictive Detection and Interpretation Software Platform for Life-Cycle Management of Spent Fuel Canisters

When nuclear reactor performance drops below desired energy levels, the radioactive waste must be properly disposed. Until deep geological disposal sites are identified for very long-term storage of this waste (1000’s of years), interim storage methods must be used for much longer than initially intended (~100 years). With hundreds of dry storage canisters approaching or already exceeding their intended design life, the Department of Energy needs an aging management program to assess the long-term integrity of these canisters. The primary objective of this work is to develop a web-based machine learning software tool for optimizing life-cycle decisions and ensuring long-term performance of welded stainless-steel dry storage canisters for the storage of spent nuclear fuel. Incorporating this tool into daily operations will promote pro-active decision-making and improved risk management to minimize the likelihood of a potentially catastrophic, failure event (loss of the canister’s containment boundary). To ground the outcome of Phase I, and demonstrate the feasibility of the approach, working prototypes for each component of proposed tool will be developed, including: (i) the artificial intelligence Bayesian decision network engine, (ii) the chloride-induced stress corrosion cracking environ-mechanical model with supporting experimental data, and (iii) the web-based delivery interface. The outcome will be a fully functional life-cycle management tool that can make more informed decisions and guide operations without disregarding the need for simplicity and computational efficiency. Further refinement of the computational engine through subsequent phases of this project will further improve the predictive capability of proposed tool. The computational framework developed here, is directly applicable to other failure modes, across all energy industry sectors, making the commercialization of this technology extremely feasible. Not only is there an economic benefit to making better decisions that lower the risk of failure, the public benefits as well.