Calculation Method of Sensitivity Coefficient Based on Statistical Sampling

被引：0
|
作者：
Ma X. [1 ]
Tang H. [1 ]
Yang L. [1 ]
Zhu S. [1 ]
Chen Y. [1 ]
机构：
[1] School of Nuclear Science and Engineering, North China Electric Power University, Beijing
来源：
Yuanzineng Kexue Jishu/Atomic Energy Science and Technology | 2019年 / 53卷 / 12期
关键词：
Moto Carlo method; Nuclear engineering; Sampling; Sensitivity coefficient;
D O I：
10.7538/yzk.2018.youxian.0873
中图分类号：
学科分类号：
摘要：
Uncertainty and sensitivity analysis of the nuclear system is of great significance for reducing the design margin of nuclear design and improving the economics of the nuclear system. The sampling method for the uncertainty and sensitivity analysis is becoming more and more important because of its simple algorithm, taking into account high-order effects, and no special requirement for response parameters. However, it was previously considered that it was difficult to analyze the sensitivity coefficient based on the statistical sampling method. The main reason was that the change of the response was caused by the simultaneous change of the multivariate, and it was difficult to determine the change of the response caused by the change of a single variable. In this paper, the theoretical formula of sensitivity coefficient analysis using statistical sampling method was deduced firstly. Then the critical formula of bare reactor with two groups approximation and the PWR cell TMI benchmark were verified, and the feasibility of the method was verified. In order to deal with the difficulty of inversion of the covariance matrix of the actual problem, the simplified covariance matrix and the unified perturbation method were proposed in this study, and the two alternative solutions were verified by using the 235U fission cross section. The analysis proves the feasibility of the method. At the same time, the influence of different sensitivity coefficients on the TMI infinite multiplication factor uncertainty was also analyzed. © 2019, Editorial Board of Atomic Energy Science and Technology. All right reserved.
引用
收藏
页码：2413 / 2419
页数：6
相关论文
共 17 条
  • [1] Wang X., Wang Y., Li X., Et al., Design of the static var compensator adaptive sliding mode controller considering model uncertainty and time-delay, Acta Phys Sin, 63, 23, (2014)
  • [2] Zhang W., Zhang H., Chen Y., Et al., Angle measurement uncertainty statistical distribution of pulsed laser quadrant photodetector, Acta Phys Sin, 66, 1, (2017)
  • [3] Yousy A., Enrico S., Nuclear Computational Science a Century in Review, (2010)
  • [4] Hu Z., Ye S., Liu X., Et al., Uncertainty quantification in the calculation of k<sub>eff</sub> using sensitity and stochastic sampling method, Acta Phys Sin, 66, 1, (2017)
  • [5] Ma X., Liu J., Xu J., Et al., Generation of correlated pseudorandom variables, Acta Phys Sin, 66, 16, (2017)
  • [6] Chiba G., Kawamoto Y., Tsuji M., Et al., Estimation of neutronics parameter sensitivity to nuclear data in random sampling-based uncertainty quantification calculations, Annals of Nuclear Energy, 75, pp. 395-403, (2015)
  • [7] Matthew R.B., Uncertainty in lattice reactor physics calculations, (2011)
  • [8] Ivanov K., Avramova M., Kamerow S., Et al., Benchmarks for uncertainty analysis in modelling (UAM) for the design, operation and safety analysis of LWRs, The 40th Annual Meeting of the Spanish Nuclear Society, (2013)
  • [9] Yamamoto A., Kinoshita K., Watanabe T., Et al., Uncertainty quantification of LWR core characteristics using random sampling method, Nuclear Science and Engineering, 181, pp. 1-15, (2015)
  • [10] Park H.J., Shim H.J., Kim C.H., Et al., Uncertainty propagation analysis for PWR burnup pin-cell benchmark by Monte Carlo code, Science and Technology of Nuclear Installations, 2, pp. 247-252, (2012)
12