Simulation optimization framework for dynamic probabilistic safety assessment

Abstract

Probabilistic safety assessment (PSA) based on event trees and fault trees has been widely used in the risk assessment of nuclear power plants. A static approach by nature, PSA has limitations to consider dynamic scenarios with time-dependent sequences and interactions. In contrast to static-based PSA, dynamic PSA has been introduced as a complementary methodology that considers dynamic scenarios between the system and human operations by interfacing physical simulation with thermal-hydraulic models for risk assessment. However, the various research on dynamic PSA has a common challenge in that the number of dynamic scenarios to be simulated increases impractically. An approach is therefore necessary to manage the number of simulations for performing dynamic PSA efficiently. The objective of this paper is to propose a simulation optimization framework using an optimization algorithm to reduce, as much as reasonably achievable, the large number of dynamic scenarios to be evaluated. The optimization algorithm is proposed to optimize the large numbers of generated dynamic scenarios while maintaining accurate risk quantification in the performance of dynamic PSA. To demonstrate the application of the proposed framework to dynamic PSA, two case studies were conducted considering loss of coolant accidents.