Knowledge-driven System Simulation for Scenario Analysis in Risk Assessment

Pietro Turati (1) , Nicola Pedroni (2) , Enrico Zio (2, 1)

1 LGI - Laboratoire Génie Industriel - EA 2606
2 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec

This chapter investigates the possibility of using system simulations for scenario analysis, to increase knowledge about the response of a system to different conditions, with the aim of identifying possible unexpected or emergent critical states of the system. Indeed, verified and validated numerical models or “simulators” offer an opportunity to increase knowledge regarding the system under analysis. In a simulation‐based scenario analysis, the analyst can run a number of simulations with different initial configurations of the system and operational parameters, and identify a posteriori those leading to critical system states. These states form the so called “critical regions” (CRs) or “damage domains” (DDs). The chapter addresses the following issues with respect to the contribution of system simulation to risk assessment: challenges in simulation‐based CR exploration; existing methods; two approaches proposed by the authors to drive scenario exploration for CR identification.

Domaines: Autre

Pietro Turati, Nicola Pedroni, Enrico Zio. Knowledge-driven System Simulation for Scenario Analysis in Risk Assessment. Terje Aven, Enrico Zio. Knowledge in Risk Assessment and Management, John Wiley & Sons, Ltd, pp.165-219, 2017, 9781119317890. ⟨10.1002/9781119317906.ch8⟩. ⟨hal-01989144⟩