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A Probabilistic Model for Estimating the Life Expectancy of Used Nuclear Fuel Containers in a Canadian Geological Repository: Effects of Latent Defects and Repository Temperature
- Source :
- Nuclear Technology; July 2020, Vol. 206 Issue: 7 p1036-1058, 23p
- Publication Year :
- 2020
-
Abstract
- AbstractLifetime predictions of used nuclear fuel containers (UFCs) destined for permanent storage in deep geological repositories are challenged by the uncertainty surrounding the environment and resultant performance of both the containers and the balance of engineered barriers over repository timescales. Much of the work to characterize the response of engineered barriers to postulated evolving environmental conditions and degradation mechanisms is limited to very short-term laboratory tests or at best in situ large-scale experiments spanning less than a few decades. While much is learned from these test programs, the fact remains that long-term performance of many tens of thousands of UFCs across a timescale of 100 000 years or more cannot be estimated with a significant degree of confidence by extrapolating single-point results of short-term experiments. This is particularly true when there is a desire to understand the progression of container failures and the timing of contaminants subsequently released into the geosphere. Lifetime predictions for UFCs require a probabilistic approach to address uncertainty. In the present work, a recently developed probabilistic corrosion model to estimate the life expectancy of copper-coated UFCs destined for a Canadian geological repository is expanded by modeling the impact of latent copper-coating defects and repository temperature on the key container life-limiting mechanism: sulfide-induced corrosion.
Details
- Language :
- English
- ISSN :
- 00295450 and 19437471
- Volume :
- 206
- Issue :
- 7
- Database :
- Supplemental Index
- Journal :
- Nuclear Technology
- Publication Type :
- Periodical
- Accession number :
- ejs53763934
- Full Text :
- https://doi.org/10.1080/00295450.2019.1700730