Development and Application of GSM for LB LOCA Analysis Method

被引：0

作者：

Cao Z. ^{[1
]}

Lin Z. ^{[1
]}

Wang T. ^{[1
]}

Liang R. ^{[1
]}

Bao J. ^{[2
]}

Lu X. ^{[1
]}

机构：

[1] China Nuclear Power Technology Research Institute, Shenzhen

[2] Nuclear and Radiation Safety Center, Ministry of Ecology and Environment, Beijing

来源：

Yuanzineng Kexue Jishu/Atomic Energy Science and Technology | 2019年 / 53卷 / 11期

关键词：

LOCA analysis method; Normality test; Parameter statistic; Uncertainty quantification; Wilk's non-parametric statistic analysis method;

D O I：

10.7538/yzk.2018.youxian.0766

中图分类号：

学科分类号：

摘要：

CGN deterministic statistic methodology (GSM) is a loss of coolant accident (LOCA) analysis method that is between the most conservative evaluation model and the best estimate evaluation model. This methodology maintains the deterministic realistic method (DRM) penalization model to ensure the conservatism of the code models and penalizing assumption to ensure a conservative plant modeling, while applying random sampling technique to statistically quantify the uncertainty range and distribution of plant status parameters, and both parametric and non-parametric methods are adopted to calculate the statistical upper bounding value (PCT95/95). When applying the GSM for CPR1000 nuclear power plant LB LOCA analysis, the margin generated can be 9% compared with DRM conservative LOCA analysis. © 2019, Editorial Board of Atomic Energy Science and Technology. All right reserved.

引用

页码：2183 / 2188

页数：5

共 6 条

[1] ECCS evaluation models, 10CFR50.46: Appendix K, (1992)
[2] Sauvage J.Y., Keldenich M., Deterministic realistic method application to a large break loss-of-coolant accident, (2001)
[3] Regulatory Guide 1.157 Best-estimate calculations of emergency core cooling system performance, (1989)
[4] Boyack B., Quantifying reactor safety margins: Application of code scaling applicability and uncertainty (CSAU) evaluation methodology to a large-break loss-of-coolant accident, NUREG/CR-5249, (1989)
[5] Devore J.L., Probability and Statistics for Engineering and Sciences, (2004)
[6] Wilk's S.S., Determination of sample sizes for setting tolerance limits, The Annals of Mathematical Statistics, 13, pp. 91-96, (1941)

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