.png){kind=icon}
返回
A framework for verifying Dynamic Probabilistic Risk Assessment models
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ress.2020.107099.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recent development of more powerful computational and technological resources has led to significant improvements in the utilization of dynamic methodologies for the Probabilistic Risk Assessment (PRA) of nuclear power plants. These methodologies integrate deterministic and probabilistic analyses and are generally referred to as Dynamic PRA (DPRA) methods. DPRA is performed through the generation and simulation of possibly thousands of different accident scenarios. To ensure the quality and the correctness of the results, DPRA models should be verified. Since DPRA generates large amount of data, a visual inspection of results to verify the correctness of the model used is neither practical nor reliable. As one of the steps for DPRA analysis, a framework is proposed to systematically explore the DPRA model prior to its simulation using statecharts which provide a graphical notation for describing dynamic aspects of system behavior. The application of the framework is illustrated using two case studies: (i) performance assessment of a heated room using the PyCATSHOO DPRA tool, and, (ii) DPRA performed with RAVEN-MAAP5-EDF codes for loss of off-site power as the initiating event in a pressurized water reactor.
Keyword:
Verification
Thermal-Hydraulic model
Dynamic Probabilistic Risk Assessment (DPRA)
Dynamic Event Tree (DET)
Statechart
YAKINDU StateChart Tools
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
R
IF:
11
论文数:
9.0K
被引数:
4.2W
机构
引用论文
分享收藏Associations between diabetes mellitus and pulmonary hypertension in chronic respiratory disease patients.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏An approximate epistemic uncertainty analysis approach in the presence of epistemic and aleatory uncertainties在存在认知和偶然不确定性的情况下，一种近似的认知不确定性分析方法
PREAI
分享收藏
查看更多内容