首页> 外文会议>International Conference on Probabilistic Safety Assessment and Management(PSAM7-ESREL'04) v.6; 20040614-20040618; Berlin; DE >Methodology for Assessment of Uncertain Input Data for a Level-3 PRA Analysis of a Nuclear Reactor Accident Using MACCS2
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Methodology for Assessment of Uncertain Input Data for a Level-3 PRA Analysis of a Nuclear Reactor Accident Using MACCS2

机译:使用MACCS2进行核反应堆事故的3级PRA分析的不确定输入数据的评估方法

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摘要

MACCS2 is a probabilistic accident consequence code that estimates the risks from the operation of nuclear installations, based on the postulated frequencies and severities of potential accidents. The risk estimates provide one of many inputs for judgments on risk acceptability and ways to reduce the excess conservatism in offsite consequence calculations. The uncertainty associated with these risk estimates has an important role in guiding efforts to reduce risks. In 1992, the US Nuclear Regulatory Commission (NRC) and the Commission of the European Communities (CEC) began a joint uncertainty analysis of the codes. As a first step, a feasibility study was conducted to determine the efficacy of evaluating a single phenomenological area of consequence calculations, atmospheric dispersion and deposition, and to decide whether the technology exists to develop credible uncertainty distributions on the input variables of the code. Expert elicitation was identified as the best technology available for developing a library of uncertainty distributions for the selected consequence parameters. The USNRC/CEC expert-elicitation results were published in six reports. This work focuses on the methodology to analyze one of the parameters in the first of these reports. Subsequent work will investigate the full set of parameters covered in the six reports.
机译:MACCS2是一个概率性事故后果代码,它根据假定的潜在事故发生频率和严重程度来估计核装置运行的风险。风险估计为风险可接受性的判断提供了众多输入之一,并提供了减少异地后果计算中过度保守的方法。与这些风险估计有关的不确定性在指导降低风险的工作中具有重要作用。 1992年,美国核监管委员会(NRC)和欧洲共同体委员会(CEC)开始对这些法规进行联合不确定性分析。第一步,进行了可行性研究,以确定评估后果计算,大气扩散和沉积的单个现象学领域的功效,并确定是否存在用于在代码的输入变量上开发可靠的不确定性分布的技术。专家启发被认为是可用于为所选结果参数开发不确定性分布库的最佳技术。 USNRC / CEC专家激发的结果发表在六份报告中。这项工作的重点是分析这些报告中第一个报告中的一个参数的方法。随后的工作将调查六份报告中涵盖的全部参数。

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