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

核废料管理组织(NWMO)负责对加拿大用过的核燃料进行长期管理。将用过的核燃料埋设在位于加拿大盾内的深层地质储存库，从概念上被确定为实现遏制和与环境和人隔离的最佳选择。提出了一种外覆铜腐蚀层的碳钢容器，用于对用过的核燃料进行密封。国家气象组织感兴趣的是在感兴趣的时间范围内旧燃料容器的预期故障率，从6万年到100万年不等。由于确定性寿命预测的不确定性，NWMO将在滑铁卢大学Ponnambalam教授和Pandey教授的指导下与研究人员合作，开发可纳入储存库关闭后安全分析的概率失效分析模型。滑铁卢大学工程学院的申请者处于有利地位，可以为旧燃料容器的故障提供一个概率模型。除了在真实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在失效分析方面的广泛多行业经验，对制造工艺的实际理解，以及在冶金和腐蚀方面的广泛实践知识。这支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失效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，将敏感性和不确定性分析与基于贝叶斯网络的先进蒙特卡洛模拟结合在一起，形成了一个经过验证和透明的基于核的概率安全分析框架，用于评估假定的失效机制。

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.****