Probabilistic safety assessment (PSA) of nuclear facilities on external multi-hazards has become a major issue after the Fukushima accident in 2011. However, the existing external hazard PSA methodology is for single hazard events and cannot cover the impact of multi-hazards. Therefore, this study proposes a methodology for quantifying multi-hazard risks for nuclear energy plants. Specifically, we developed an efficient multi-hazard PSA methodology based on the probability distribution-based Boolean algebraic approach and sampling-based method, which are currently single-hazard PSA methodologies. The limitations of the probability distribution-based Boolean algebraic approach not being able to handle partial dependencies between the components are solved through this sampling-based method. In addition, we devised an algorithm that was more efficient than the existing algorithm for improving the limits of the current sampling-based method, as it required a significant computational time. The proposed methodology was applied from simple examples to single- and multi-hazard PSA examples of actual nuclear power plants. The results showed that the proposed methodology was verified in terms of accuracy and efficiency perspectives. Regarding the sampling-based method, it was confirmed that the proposed algorithm yielded fragility and risk results that have similar degrees of accuracy, even though it extracted a smaller number of samples than the existing algorithm.

The objective of this study is to propose a model that can predict the seismic performance of slope relatively accurately and efficiently by using machine learning methods. Probabilistic seismic fragility analyses of the slope had been carried out in other studies, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, the traditional statistical linear regression analysis showed a limit that could not accurately represent such nonlinear slope seismic performances. To overcome this limit, in this study, we used three machine learning methods (i.e., support vector machine (SVM), artificial neural network (ANN), Gaussian process regression (GPR)) to generate prediction models of the slope seismic performance. The models obtained through the machine learning methods basically showed better performance compared to the models of the traditional statistical methods. The results of the SVM showed no significant performance difference compared with the results of the nonlinear regression analysis method, but the results based on the ANN and GPR showed a remarkable improvement in the prediction performance over the other models. Furthermore, this study confirmed that the GPR-based model predicted relatively accurate seismic performance values compared with the model through the ANN.

The purpose of this study is to evaluate vibration characteristics according to the foundation type of an emergency diesel generator (EDG) of a nuclear power plant (NPP). For the purpose of this study, EDGs whose types are the same but whose foundation types are different were selected and measured for their operational vibrations. The target EDG systems were located in Yonggwang unit 5 and Ulchin unit 3 in Korea. Both EDG systems are the 16PC2—5V400 model type which was manufactured by the SEMT Pielstick corporation. The foundation systems of the Yonggwang 5 and Ulchin 3 units were an anchor bolt system and a Coil Spring-Viscous Damper system, respectively. The vibrations were measured in the case of before and during an operation for the two kinds of EDG system. A total of eight accelerometers were used for the vibration measurements. Six of the accelerometers were used for the foundation system, one accelerometer for the EDG body and the remaining accelerometer was for the isolated floor. The relative displacements between the isolated floor and foundation system were also measured to determine the complete movement of the EDG system in the case of using the Coil Spring-Viscous Damper system. The vibration reduction effects were evaluated and compared with the vibration responses for the foundation and the isolated floor, before and during an operation for the vibration measurement cases. The results of this research revealed that the Coil Spring-Viscous Damper system can reduce a vibration by over 90%. Although the Coil Spring-Viscous Damper system induced a rolling movement for the whole system, this movement of the whole EDG system was more stable when compared to that of an anchor bolt system.

Abnormal weather conditions due to climate change are currently increasing on both global and local scales. It is therefore important to ensure the safety of the areas where major national facilities are located by analyzing risk quantitatively and re-evaluating the existing major facilities, such as nuclear power plants, considering the load and capacity of extreme flood conditions. In this study, a risk analysis method is developed that combines flood hazard curves with fragility curves using hydraulic and hydrological models by GIS tools and the @RISK model for the probabilistic flood analysis of nuclear power plant sites. A two-dimensional (2D) analysis is first carried out to estimate flood depths in various watershed scenarios, and a representative hazard curve for both external and internal flooding is made by applying a verified probability distribution type for the flood watersheds. For the analysis of flooding within buildings, an internal grid is constructed using GIS with related design drawings, and based on the flood depth results of the 2D analysis, a hazard curve for the representative internal inundation using a verified probability distribution type is presented. In the present study, walkdowns with nuclear experts are conducted around the nuclear power plant area to evaluate the fragile structures and facilities under possible flooding. After reviewing the 2D inundation analysis results based on the selected major equipment and facilities, the zones requiring risk assessment are re-assigned. A fragility curve applying probability distribution for the site’s major equipment and facilities is also presented. Failure risk analysis of the major facilities is then conducted by combining the proposed hazard and fragility curves. Results in the form of quantitative values are obtained, and the indicators for risks as well as the reliability and optimal measures to support decision-making are also presented. Through this study, it is confirmed that risk assessment based on the proposed probabilistic flood analysis technique is possible for flood events occurring at nuclear power plant sites.

Seismically isolated nuclear power plants (NPPs) can provide substantial benefits towards reducing the failure probability of NPPs, especially for beyond design basis earthquake shaking. One risk posed by seismic isolation is the potential for pounding to a stop or moat wall, with currently little guidance provided by design standards on how to address this concern. In this paper, a structural model of an isolated NPP based on the Advanced Power Reactor 1400 MW is enveloped with moat walls and advanced bearing models. The bearing models account for large strain behavior through failure based on full-scale experiments with lead rubber bearings (LRBs). Using these analytical models and a measured ultimate property diagram from LRB failure tests, the range of clearance to the stop considering the performance criteria for the NPP is investigated. Although the analysis results are dependent on the particular models, ground motions, and criteria employed, this research provides an overview of the seismic response and performance criteria of an isolated NPP considering the clearance to the stop.

Nuclear power plants are required to maintain operation after an earthquake, leading to a safe shutdown if necessary. In the case of a loss of offsite power, the onsite emergency diesel generator is critical to ensure procedural operations of the nuclear power plant. As a means to reduce the overall seismic risk, a three-dimensional seismic protection system is proposed to enhance the seismic performance of the emergency diesel generator. The proposed seismic isolation system decouples the horizontal and vertical components of shaking and considers available hardware to achieve an effective isolation solution over the range of excitation frequencies considered. Numerical analysis of the proposed system demonstrates a reduction in seismic demands on the emergency diesel generator and provides a higher safety margin than conventional base installation procedures. Umbilical lines that cross the isolation plane are considered and impose additional constraints on the displacement capacity of the isolation system. However, increasing the displacement capacity of these components can significantly increase the safety margin against failure. The seismic protection system can be customized depending on the seismic hazard and application to different seismic regions.