Probabilistic Safety Analysis (PSA) is a systematic approach to evaluate system safety. The conventional PSA methods can treat the complex dynamic interactions between processes and stochastic events only through approximations. The emphasis of this thesis is put on the development of a method for evaluation of the dynamic systems. After investigating the conventional and dynamic PSA methods, an optimization method – Monte Carlo Simulation (MCS) using Adaptive Importance Sampling (AIS) - is presented. With the utilization of this optimization method the accident scenarios can be modeled and analyzed more precisely. By adjusting the sampling density functions, the computational time can be reduced and the accuracy of the estimate can be improved. The applicability of this optimization method in the field of Reactor Safety has been demonstrated with a transient in a pressurized water reactor.
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Probabilistic Safety Analysis (PSA) is a systematic approach to evaluate system safety. The conventional PSA methods can treat the complex dynamic interactions between processes and stochastic events only through approximations. The emphasis of this thesis is put on the development of a method for evaluation of the dynamic systems. After investigating the conventional and dynamic PSA methods, an optimization method – Monte Carlo Simulation (MCS) using Adaptive Importance Sampling (AIS) - is pres...
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