Dynamic scenario quantification for level 2 PRA of sodium-cooled fast reactor based on continuous Markov chain and Monte Carlo method coupled with meta-model of thermalhydraulic analysis
Dynamic scenario quantification for level 2 PRA of sodium-cooled fast reactor based on continuous Markov chain and Monte Carlo method coupled with meta-model of thermalhydraulic analysis
复制标题
基于连续马尔可夫链和蒙特卡罗方法结合热工水力分析元模型的钠冷快堆二级PRA动态场景量化
DOI：
10.1080/00223131.2018.1445564
复制
发表时间：
2018
影响因子：
1.2
通讯作者：
Akira Yamaguchi
中科院分区：
文献类型：
--
作者：
Sunghyon Jang;Akira Yamaguchi
在概率风险评估（PRA）中，事件树（ET）方法被广泛用于量化导致堆芯损坏和裂变产物释放的事故情景。然而，目前的方法使用的ET方法是不适用的，以评估事故进展的动态特性时，事故进展是时间依赖性和标题在ET事件之间有相互依赖性。因此，在本发明中，为了更好地评价反应堆事故情景，需要一种动态的事故情景量化方法。针对这一需求，将连续马尔可夫链和蒙特卡罗（CMMC）方法与钠冷快堆（SFR）的热工水力分析程序相结合，建立了一种2级反应堆事故情景的动态情景量化方法。CMMC方法应用于保护热沉损失（PLOHS）的事故情景分析，并与实验结果进行了比较。事故的SFR分析动态场景量化。耦合方法需要大量的计算成本，并且难以通过与现有工厂状态分析代码的结果进行比较来量化整个事故场景。因此，元分析耦合方法，提出了获得合理的计算成本的动态场景量化。此外，分类方法用于以透明的方式描述分析结果。
In probabilistic risk assessment (PRA), an event tree (ET) methodology is widely used to quantify accident scenarios which result in core damage and fission products release. However, the current approach using the ET methodology is not applicable to evaluate dynamic characteristics of accident progression, when the accident progression is time-dependent and headings in the ET have inter-dependency between events. Thus, a dynamic approach of accident scenario quantification is necessary to evaluate more realistic PRA.This research addressed this need by developing a dynamic scenario quantification method for the level 2 PRA by coupling of Continuous Markov chain and Monte Carlo (CMMC) method and a plant thermal–hydraulic analysis code for a sodium-cooled fast reactor (SFR).The CMMC method is applied to protected loss of heat sink (PLOHS) accident of the SFR to analyze dynamic scenario quantifications. The coupling method requires heavy computational cost and it makes difficult to quantify the whole accident scenarios by comparing the results from existing plant state analysis codes. Thus, a meta-analysis coupling method is proposed to obtain dynamic scenario quantifications with reasonable computational cost. Also, a categorizing method is used to depict analytical results in a transparent manner.