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Model and boundary condition uncertainty analysis of PWR LBLOCA transient event in RELAP5/MOD3.3 code
Conference paper

Model and boundary condition uncertainty analysis of PWR LBLOCA transient event in RELAP5/MOD3.3 code

Chih-Chia Chiang, Hao-Chun Chang, Jong-Rong Wang, Chunkuan Shih, Shao-Wen Chen and Yu-Ming Feng
18th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2019, pp.6244-6256
2019

Abstract

LBLOCA PWR RELAP5/MOD3.3 UNCERTAINTY Nuclear Energy and Engineering Instrumentation
Maanshan nuclear power plant (NPP), located on the southern coast of Taiwan, is a pressurized water reactor (PWR) designed and built by Westinghouse for Taiwan Power Company (Taipower, TPC). The RELAP5/MOD3.3 model of MNPP had been developed with SNAP interface in 2015. Several start-up tests, loss of flow events and loss of coolant accident (LOCA) events have been performed to ensure the capability of this model. In addition, to further access the reliability of this model, uncertainty analysis for LOCA event had been conducted. There were six uncertainty parameters which were all related to the emergency core cooling system (ECCS) considered in the past research. However, there were only parameters related to the safety injection system concerned. Other boundary-condition uncertainties are not included. Hence, a more completed uncertainty concern will be performed in this research. In addition to the boundary condition uncertainty, model uncertainty related to two phase flow model in RELAP5 code are further considered. RELAP5 code had developed seven interfacial drag coefficient uncertainty factors, including bubbly flow drag, slug flow drags, annular-mist flow drags, dispersed flow drag, re-flood drag and two-phase friction and form loss. With randomly adjusting this developed two-phase flow drag factors, the model uncertainty can be also considered. After realizing the effect of these two-phase flow model uncertainties, a completed simulation model which considers both the boundary condition and model uncertainty will be developed. As a result, the predictions of the simulation model may become closer to the real world.

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