Dynamic hybrid reliability studies of a decay heat removal system
Abstract
Some critical safety systems exhibit the characteristics of hybrid stochastic class whose performance depends on the dynamic interactions of deterministic variables of physical phenomena and probabilistic variables of system failures. However, conventional probabilistic safety assessment (PSA) method involves static event and linked fault tree analysis and does not capture the dynamic interactions of such hybrid stochastic systems. Additionally, the existing dynamic PSA methods do not considers any repair possibility of some failed components during safety assessment. To address these issues, this paper presents a dynamic hybrid reliability assessment scheme for performance studies of repairable nuclear safety systems during a mission time. This scheme combines the features of reliability block diagram (RBD) for system compositions and partial differential equations for system physics using a customized stochastic hybrid automata tool implemented on Python platform. A case study of decay heat removal (DHR) systems has been performed using the introduced scheme. The impacts of failure rates and repair rates on sodium temperature evolution over a mission time have been analyzed. The results provide useful safety insights in mission safety tests of DHR systems. In sum, this work advances the dynamic safety assessment approach for complex system designs including nuclear power plants.
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