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Degradation Analysis and Risk-Informed Management of Feedwater System in Nuclear Power Plants

Volume 3, Number 1, January 2007 - Paper 13  - pp. 149 - 158

WEN-FANG WU1, JANG-SHYONG YOU2, HUNG-TA KUO1 AND CHIH-HSIEN WU1

1Department of Mechanical Engineering and Institute of Industrial Engineering, National Taiwan University, Taipei, Taiwan 10617
2Department of Plastic Design, National Taipei University of Education, Taipei, Taiwan 10671

(Received from the Guest Editor on July 25, 2006)


Abstract:

The flow accelerated corrosion (FAC) occurred on the shell side near the inlet nozzle of the feedwater heater is considered the major failure mechanism of the feedwater system in a nuclear power plant. Therefore, a semi-empirical formula proposed by Siemens/KWU is used in the present study to predict the extent of corrosion of the heater-wall caused by FAC. The formula is proved to be able to predict the amount of corrosion very accurately, and sensitivity analysis indicates that, aside from other factors, fluid velocity, steam quality and oxygen content have significant influences on the corrosion rate. A Monte Carlo method that considers randomness of several parameters is employed to simulate amounts of wall-corrosion of individual feedwater heaters and evaluate their relative risks. Under the assumption that risks for other components of the feedwater system are also known, probabilistic-risk-assessment (PRA) is applied to the entire feedwater system. Several importance measures including Fussell-Vesely (FV), risk achievement worth (RAW), and difference importance measure (DIM) are adopted for risk ranking of individual heaters and other components. It is found that although RAW is simpler to use but its result is not so distinctive among individual components as compared to the other two measures. Since the risk-significance of each component obtained or assumed evolves with time, a proper and economical inspection program that emphasizes certain components at certain times can therefore be made.

 

References: 10

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