| Description: |
Classic methods of probabilistic safety assessments (PSA), often used in nuclear domain, even when they go beyond the static approaches, remain, for historical reasons, confined in discrete events framework and quickly reach their limits when finer dependability assessment is needed. These methods neglect a large part of information about our systems. In particular, physical phe- nomena modeling, are not explicitly present in PSA and are often replaced by so called conserva- tive assumptions. Yet several attempts have been made to remedy this. But their implementations are sometimes based on a tool dedicated to the discrete events modeling into which some continuous model- ing functionalities are intruded. Conversely, the starting point may be a tool dedicated to the 0D physical modeling patched with some discrete events modeling functionalities. In both cases the results are not satisfactory because their use for the assessment of even simple systems often requires some convoluted modeling which may be so difficult to maintain.. PyCATSHOO brings an original solution in that it offers a method and a tool where the two paradigms, continuous deterministic on the one hand and discrete stochastic on the other hand, are natively integrated. It also provides a lot of convenient tools which make user feel at ease with the increase of modeling complexity due to this integration. To ease the understanding of what PyCATSHOO is, we begin this document by a non exhaustive list of some studies carried out with PyCATSHOO. |
