Selection and implementation of deterministic and probabilistic models for the prediction of stress corrosion cracking in used nuclear fuel containment material

Abstract

Stainless steel canisters encased in vented concrete overpacks are used to store used nuclear fuel at interim spent fuel storage installations in the United States. These storage systems are exposed to the elements. There is concern that, over time, a deliquesced salt film could develop on the stainless steel canister. Such a film can create a corrosive environment in stainless steels. If a stress is present on or in then material, stress corrosion cracking (SCC) could be a possibility. Because the canister welds are not stress-relieved in order to avoid sensitization of the steel, residual stresses are expected to be present in the canisters. Thus, there is interest in determining the likelihood that (a) a sufficiently corrosive film develops on a stainless steel used fuel canister (b) there are sufficiently high stresses in the material at the location of the corrosive film (c) SCC initiates and (d) the crack propagates through the canister wall, resulting in canister failure. This thesis begins with the assumption that a corrosive film has developed on the canister surface, and pits have begun to initiate. It investigates various methods of modeling SCC in the canister wall after the point of corrosion pit initiation. An extensive literature review was carried out in order to understand the different SCC models that currently exist in the literature. A figure-of-merit was developed to decide which models were the most likely to be helpful to the modeling of SCC in used fuel canisters. The figure-of-merit was then used to select the most promising models. These models were then used to write MATLAB@ simulations that could be used to predict time-to-failure in canisters due to SCC once corrosion pits have begun to grow. The results of these simulations are then considered and compared, and used to inform recommendations for future development of a useful predictive model of SCC in used fuel canisters.