期刊
NUCLEAR ENGINEERING AND TECHNOLOGY
卷 48, 期 3, 页码 684-701出版社
KOREAN NUCLEAR SOC
DOI: 10.1016/j.net.2016.01.016
关键词
Gaussian Process Model; Large-Break Loss-of-Coolant Accident (LBLOCA); Success Criteria; Safety Margin
资金
- Nuclear Research & Development Program of the National Research Foundation of Korea (NRF)
- Korean government, Ministry of Science, ICT & future Planning (MSIP) [NRF-2012 M2A8A4025989]
The Gaussian process model (GPM) is a flexible surrogate model that can be used for nonparametric regression for multivariate problems. A unique feature of the GPM is that a prediction variance is automatically provided with the regression function. In this paper, we estimate the safety margin of a nuclear power plant by performing regression on the output of best-estimate simulations of a large-break loss-of-coolant accident with sampling of safety system configuration, sequence timing, technical specifications, and thermal hydraulic parameter uncertainties. The key aspect of our approach is that the GPM regression is only performed on the dominant input variables, the safety injection flow rate and the delay time for AC powered pumps to start representing sequence timing uncertainty, providing a predictive model for the peak clad temperature during a reflood phase. Other uncertainties are interpreted as contributors to the measurement noise of the code output and are implicitly treated in the GPM in the noise variance term, providing local uncertainty bounds for the peak clad temperature. We discuss the applicability of the foregoing method to reduce the use of conservative assumptions in best estimate plus uncertainty (BEPU) and Level 1 probabilistic safety assessment (PSA) success criteria definitions while dealing with a large number of uncertainties. Copyright (C) 2016, Published by Elsevier Korea LLC on behalf of Korean Nuclear Society.
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