Modelling a feed-water control system of a steam generator in the framework of the dynamic reliability
Résumé
The paper deals with the exploration of an industrial complex system behaviour and its probabilistic safety assessment (PSA). The main purposes are to build a model which realistically represents the system structure, to carry out the Monte Carlo study of the system behaviour and to perform the analysis of system reliability. The complexity of the system consists in its structure, size, dynamic operational behaviour, complex interactions between the system and its environment, etc. The theoretical framework chosen is the dynamic reliability approach allowing to account for all of these properties. The Stochastic Hybrid Automaton (SHA), seen as a flexible representation of the Piecewise Deterministic Markov Process, is a suitable tool to simultaneously represent continuous and discrete, stochastic and deterministic phenomena, and is thus employed as a formal model to draw the system structure and behaviour. This formal model (SHA) is effectively implemented in Scilab/Scicos open source freeware. This tool is efficient to simulate both the differential equations and discrete state changes when events occur. The differential equations represent the continuous evolution of physical variables describing the dynamic operational behaviour. The discrete states describe the various operating and dysfunctional modes of the system. A real-life system is used to perform a case study of the proposed approach. Precisely, within the French research project APPRODYN (APPROches de la fiabilit'e DYNamique pour mod'eliser des syst'emes critiques - Dynamic reliability approaches to model critical systems), the feed-water control system of a steam generator of a pressurised water nuclear reactor is modelled. This system consists of different interacting components which simultaneously function according to the power demand. The power is a continuous variable representing operational behaviour. The built behavioural model and performed Monte Carlo simulations are used to study the trajectories of the system behaviour and to evaluate the probability of critical events occurrence.
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