摘要
为了评估复杂事故现象物理模型参数(输入)的不确定性,提出了基于随机森林算法结合粒子群优化Kriging(PSO-Kriging)代理模型和Sheather-Jones优化核密度估计法(KDE-SJ)非参数统计的反向不确定性量化方法,并应用于大破口事故再淹没现象的模型评估。通过将系统程序的计算结果(输出)与Flooding Experiments with Blocked Arrays(FEBA)实验数据的一致性程度作为随机森林算法的分类标准,得到了模型参数的概率密度分布。验证结果表明在概率密度分布上随机抽样93组计算得到的95%不确定度带可以完全包络实验数据,但利用众数或均值对模型的标定效果可能不如贝叶斯方法得到的最大后验均值。
In order to assess the uncertainty of physical models(inputs)of complex accidents,an inverse uncertainty quantification method based on Random Forest algorithm combined with PSO-Kriging surrogate model and KDE-SJ nonparametric statistics is proposed,and it is applied to the model assessment of reflooding in large breach accidents.The probability density distributions of the model parameters were obtained through the degree of consistency between the calculation results(output)of the system program and the FEBA experimental data as a classification criterion for the Random Forest algorithm.The validation results show that the 95%uncertainty bands obtained by randomly sampling 93 groups of calculations on the probability density distributions can completely envelope the experimental data,but the calibration effect of the model using mode or mean may not be as good as the maximum posterior mean obtained by Bayesian method.
作者
雷盟
李冬
张紫悦
郝饶
Lei Meng;Li Dong;Zhang Ziyue;Hao Rao(College of Energy and Mechanical Engineering,Shanghai University of Electric Power,Shanghai,201306,China;Xi’an Electric Power College,Xi’an,710032,China)
出处
《核动力工程》
北大核心
2025年第2期98-106,共9页
Nuclear Power Engineering
关键词
反向不确定性量化
随机森林
代理模型
非参数统计
再淹没现象
Inverse uncertainty quantification
Random Forest
Surrogate model
Nonparametric statistics
Reflooding phenomenon
