A steam generator tube rupture (SGTR) is a typical failure scenario in a nuclear power plant (NPP) to be analysed. Due to the inherent importance of the process dynamics, it is a suitable candidate for a dynamic probabilistic safety analysis (Dynamic PSA). A dynamic PSA performed with the GRS tool MCDET (Monte Carlo Dynamic Event Tree) has shown the abundance of probabilistic information which can be extracted if the complex dynamic process is properly modelled. MCDET allows to consider discrete uncertain parameters by the dynamic event tree (DET) approach and continuous uncertain parameters by applying Monte Carlo simulation in combination with the DET approach. This provides the opportunity to identify new, previously unnoticed event sequences leading to undesired system states. One of the questions arising is how this information can be fed back to a classic PSA.

The prime implicants of dynamic event trees provide such a link between dynamic and classic PSA. Extracted from MCDET results they can in turn be used to generate classic event trees or subtrees. In this context, prime implicants are the minimal set of characteristic discrete conditions leading to the undesired end state of a system. In this paper it is demonstrated how, namely by using machine learning algorithms and an adapted prime implicant algorithm, the information produced in the MCDET analysis of a SGTR scenario is analysed to extract prime implicants. In addition, it is shown how these prime implicants are translated into the event tree logic of a classic PSA using an updated version of the GRS script-based PSA tool pyRiskRobot.