Scenario development for safety assessment in deep geologic disposal of high‐level radioactive waste and spent nuclear fuel: A review

放射性废物 期货合约 风险分析(工程) 风险评估 过程(计算) 高水平废物 概率逻辑 乏核燃料 计算机科学 环境科学 工程类 废物管理 业务 计算机安全 操作系统 人工智能 财务
作者
Kristopher L Kuhlman,J. Bartol,Alexander Carter,Andree Lommerzheim,Jens Wolf
出处
期刊:Risk Analysis [Wiley]
被引量:1
标识
DOI:10.1111/risa.14276
摘要

Radiation and radioactive substances result in the production of radioactive wastes which require safe management and disposal to avoid risks to human health and the environment. To ensure permanent safe disposal, the performance of a deep geological repository for radioactive waste is assessed against internationally agreed risk-based standards. Assessing postclosure safety of the future system's evolution includes screening of features, events, and processes (FEPs) relevant to the situation, their subsequent development into scenarios, and finally the development and execution of safety assessment (SA) models. Global FEP catalogs describe important natural and man-made repository system features and identify events and processes that may affect these features into the future. By combining FEPs, many of which are uncertain, different possible future system evolution scenarios are derived. Repository licensing should consider both the reference or "base" evolution as well as alternative futures that may lead to radiation release, pollution, or exposures. Scenarios are used to derive and consider both base and alternative evolutions, often through production of scenario-specific SA models and the recombination of their results into an assessment of the risk of harm. While the FEP-based scenario development process outlined here has evolved somewhat since its development in the 1980s, the fundamental ideas remain unchanged. A spectrum of common approaches is given here (e.g., bottom-up vs. top-down scenario development, probabilistic vs. bounding handling of uncertainty), related to how individual numerical models for possible futures are converted into a determination as to whether the system is safe (i.e., how aleatoric uncertainty and scenarios are integrated through bounding or Monte Carlo approaches).
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