Advancements in Probabilistic Safety Assessment of Nuclear Energy for Sustainability

Since the publication of the first comprehensive Probabilistic Safety Assessment (PSA) study—known as WASH-1400—in the US, PSA has developed into an effective and systematic method of identifying hazards, and evaluating and prioritizing the risks in nuclear facilities.

Meanwhile, in addition to large-sized power plants, which are traditionally considered forms of nuclear energy, small or micro-modular reactors are now emerging, and the application fields of nuclear energy are expanding not only on the ground, but also into space, the sea, and some remote places.

In order for PSA to respond to upcoming technologies, it should be able to simulate complex phenomena flexibly rather than using a standardized approach and quantify uncertainty caused by potential variation. Such complexity eventually comes from spatiotemporal dependencies. Therefore, it is expected to proceed in the form of a digital twin in which simulation by a physical model and reliability evaluation are integrated.

This book aims to share innovative ideas that go beyond the role of PSA to evaluate risks, finds hidden factors, and creates emergency response procedures on its own.

Baek et al. [1] presented an application case of a dynamic fault tree that can consider the spatiotemporal dependence of the fault tree. In particular, the power system of a nuclear station is a representative facility in which dependency issues appear complex, and new insights that can be discovered through the dynamic fault tree were dealt with. Meanwhile, Shah et al. [2] proposed a dynamic method that considers the time dependence of FLEX by reflecting the dynamic characteristics of event trees, and conducted a case study on design basis accidents.

Arigi et al. [3] dealt with the operator’s diagnosis error, which is one of the main causes of spatiotemporal dependence. In particular, a diagnostic error model evaluated in a digital main control room was presented.

Earthquake-related issues are heavily involved in spatiotemporal dependence. Choi et al. [4] presented a method to convert the impact of earthquakes into the notion of CCFs. In order to transform the dependence expressed in seismic correlation into CCFs that can be utilized in the failure tree, he described a systematic numerical analytical methodology. Jung [5] pointed out potential errors that may occur during the evaluation of combinations for seismic events and suggested a method to increase the accuracy in connection with seismic correlation in multi-unit PSA, especially in which operation modes are considered complex. Kim et al. [6] performed a sensitivity analysis for seismic hazard bins used for quantification.

Song et al. [7] presented an analytic approach that can be used in quantifying the uncertainty of the PSA model, and Di Maio et al. [8] mentioned various methods that can be used to analyze the reliability of the passive system in a review paper.

The aforementioned Special Issue aims to introduce and share potential enabling methodologies to make breakthroughs for more realistic PSA. The Issue provides a good opportunity to intensively deal with challenging areas of development and share them with worldwide distinguished experts. We are trying to pioneer upcoming potentials of the PSA such that nuclear energy can contribute to mankind in a clean and green manner.