摘要
不确定性分析能够量化PSA模型中的不确定性因素从而为决策者提供决策支持。提出一种基于蒙特卡罗模拟的故障树不确定性分析方法用于求得置信区间的上下确界,该方法将模拟求解过程中的对超越方程的求解问题转化成优化问题,并充分利用了优化问题中目标函数的属性特征,选择黄金分割点法来进行一维搜索求解。在此基础上为加速收敛对黄金分割点算法做了改进。该方法已应用于自主开发的PSA分析软件RiskA中。并将RiskA不确定性分析结果和其他的PSA软件进行了校核,校核结果表明这种方法是正确的。
Uncertainty analysis which gives support to decision-making is an indispensable phrase in PSA quantitative analysis. A low complexity method based on Monte Carlo simulation for uncertainty analysis is proposed to find the tightest possible upper and lower bounds of the confidence interval. The problem of finding the solution to a transcendent function is formulated as an optimization problem. The golden mean algorithm is selected to solve this optimization model whose attributes are utilized adequately. To improve the performance of the algorithm, several techniques are used. This method was implemented in the home-developed PSA code RiskA. This method＇s correctness was checked out by comparing the results between RiskA and RiskSpectrum.
出处
《核科学与工程》
CSCD
北大核心
2006年第4期353-357,共5页
Nuclear Science and Engineering
