Assessment of the long term performance of high level radioactive fuel containers in Canadian shield

核废物管理组织（NWMO）负责长期管理加拿大使用过的核燃料。在概念上，将用过的核燃料安放在加拿大地盾的深层地质处置库中被确定为实现封闭和与环境和人类隔离的最佳选择。提出了一种用于盛装用过的核燃料的碳钢容器，该容器外覆铜腐蚀层。NWMO感兴趣的是在感兴趣的时间尺度上使用过的燃料容器的预期故障率，其范围从60，000年到100万年。由于确定性寿命预测的不确定性，西北气象组织将与滑铁卢大学Ponnambalam教授和Pandey教授指导下的研究人员合作，开发一个概率故障分析模型，该模型可纳入处置库关闭后的安全分析。滑铁卢大学工程学院的申请人能够很好地为使用过的燃料容器的故障提供概率模型。除了在实际核部件概率评估方面的丰富实践经验外，研究小组还为这项任务带来了广泛的故障分析多行业经验，对制造工艺的实际理解以及冶金和腐蚀方面的广泛实践知识。这个由学术研究人员和工业科学家组成的独特团队为旧燃料容器的概率故障分析建模提供了独特的视角，并保证了一个平衡的解决方案，将灵敏度和不确定性分析沿着基于贝叶斯网络的高级蒙特卡罗模拟集成到一个经过验证的透明核概率安全分析框架中，用于评估假设的故障机制。

The Nuclear Waste Management Organization (NWMO) has the responsibility for the long-term management of Canada's used nuclear fuel. The emplacement of used nuclear fuel in a deep geological repository situated in the Canadian Shield has been identified conceptually as the best option to achieve containment and isolation from the environment and people. A carbon steel container coated with an external copper corrosion layer is proposed for the containment of the used nuclear fuel. Of interest to the NWMO is the expected failure rate of the used fuel containers over the time scales of interest, which range from 60,000 years to 1 million years. Owing to the uncertainty in deterministic life predictions, the NWMO will work with researchers under the directions of Professors Ponnambalam and Pandey of the University of Waterloo to develop a probabilistic failure analysis model that can be incorporated into post-closure safety analysis of the repository. The applicants in the Faculty of Engineering at the University of Waterloo are well positioned to deliver on a probabilistic model for the failure of the used fuel containers. In addition to significant practical experience with probabilistic assessments of real-life nuclear components, the research group brings to this task extensive multi-industry experience in failure analysis, practical understanding of manufacturing processes, and broad hands-on know-how in metallurgy and corrosion. The unique makeup of this team of academic researchers and industrial scientists offers a distinct perspective into the probabilistic failure analysis modelling of the used fuel containers and guarantees a well-balanced solution, integrating sensitivity and uncertainty analysis along with advanced Bayesian Network based Monte Carlo simulations into a proven and transparent nuclear-based probabilistic safety analysis framework for the assessment of postulated failure mechanisms.