The MCDET method for probabilistic dynamics is a combination of Monte Carlo (MC) simulation and the Discrete Dynamic Event Tree (DDET) approach. The implementation of MCDET works in tandem with any appropriate deterministic dynamics code.

MCDET was developed to achieve a more realistic modeling and analysis of complex system dynamics in the framework of probabilistic safety analyses. It is capable of accounting for aleatory (stochastic) uncertainties, which are the reason why the safety assessment is probabilistic, and for epistemic (state-of-knowledge) uncertainties, which determine the precision of the probabilistic assessment. In MCDET, discrete aleatory variables are generally treated by the DDET approach, whereas continuous aleatory variables are handled by MC simulation. For each set of values provided by the MC simulation, MCDET generates a new DDET.

The paper gives a description of the MCDET method and an overview of the results that may be obtained from its application. The results presented were derived from an application of MCDET in combination with the deterministic dynamics code MELCOR for integrated severe accident simulation. For illustration purposes, the consequences in a German nuclear power plant after a station blackout were analyzed.