Assessment of Kernel Regression Applied to Point Kinetic Model with Feedback for CSAU Methodology

摘要：

In this paper, we investigates the quantification of the margin of safety of a nuclear power plants (NPPs) based on the code scaling, applicability, and uncertainty (CSAU) evaluation methodology developed by the United States Nuclear Regulatory Commission (USNRC) Ref. 1, to postulate loss of coolant accident (LOCA). According to the 1988 revised, Code of Federal Regulation （10 CFR Part 50. 46 Appendix K), Best Estimate analyses can be used to quantify the margin of safety criteria with high level of certainty. The USNRC proposed 95/95 safety criteria. In general, the best estimate analysis involves polynomial response surface to represent the computer code response to the variation of the dominant input parameters. In this paper, we use a single input single output point kinetics model (PKM) and estimate the response of the model using nonparametric kernel regression for discontinuous regression functions. In the past, researches involving the quantification of the margin of safety criteria of NPPs for design basis accident analysis have assumed smooth regression function. Also, there has been no specific effort to quantify the uncertainty introduced by the mathematical models. We in this paper relax the assumption of smooth regression function and quantify the uncertainty introduced by kernel regression by using the PKM along with 3 node lumped thermal model of a fuel pin with feedback.

关键词： Uncertainty Analysis Reactor Safety Kernel Regression Best Estimate Analysis

作者: Agarwal Vivek Bertodano Martin Ransom Victor Tsoukalas Lefteri

作者单位: School of Nuclear Engineering, Purdue University, 400 Central Drive, West Lafayette, IN-47907, USA

会议类型: 国际会议

会议名称: ISSNP2008、CSEPC、ISOFIC2008(第二届21世纪和谐核电系统国际会议、第四届电厂控制中认知系统工程方法国际会议暨第三届未来核电厂仪表与控制国际会议)

会议地点: 哈尔滨

会议语种:英文

页码: 329-335

在线出版日期: 2008-09-08（万方平台首次上网日期，不代表论文的发表时间）