Improvement of sensitivity and uncertainty analysis capabilities of generalized response in Monte Carlo code RMC

被引：5
|
作者：
Shi, Guanlin [1 ]
Guo, Yuchuan [1 ]
Jia, Conglong [1 ]
Feng, Zhiyuan [1 ]
Wang, Kan [1 ]
Huang, Shanfang [1 ]
Cheng, Quan [1 ]
机构：
[1] Tsinghua Univ, Dept Engn Phys, Beijing 100084, Peoples R China
来源：
ANNALS OF NUCLEAR ENERGY | 2021年 / 154卷
基金：
中国国家自然科学基金;
关键词：
Monte Carlo; Sensitivity analysis; Superhistory based generalized perturbation theory formulation; Reaction rate; Uncertainty analysis;
D O I：
10.1016/j.anucene.2020.108099
中图分类号：
TL [原子能技术]; O571 [原子核物理学];
学科分类号：
0827 ; 082701 ;
摘要：
The capability of performing nuclear data sensitivity and uncertainty analysis of reaction rates has been developed in continuous-energy Reactor Monte Carlo (RMC) code. The sensitivity coefficients of reaction rates are calculated by using new generalized perturbation theory (GPT) formulation which is also implemented in McCARD. The superhistory based generalized perturbation theory (SH-GPT) formulation is developed in this paper to overcome the huge memory consumption problem encountered by the new GPT formulation. This newly developed capability is tested on several benchmark problems. The sensitivity coefficients are shown to agree within 5% of reference results for most cases. Moreover, the region-specified sensitivity analysis capability enables RMC to be the first continuous-energy Monte Carlo code that can use the first-order uncertainty quantification method to perform uncertainty analysis of the power distribution. The uncertainty results calculated by first-order uncertainty quantification method also agree well with those obtained by using stochastic sampling method. (C) 2020 Elsevier Ltd. All rights reserved.
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页数：9
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