• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.138-145
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• 2019

With the application of digital technology to safety-critical infrastructures, cyber-attacks have emerged as one of the new dangerous threats. In safety-critical infrastructures such as a nuclear power plant (NPP), a cyber-attack could have serious consequences by initiating dangerous events or rendering important safety systems unavailable. Since a cyber-attack is conducted intentionally, numerous possible cases should be considered for developing a cyber security system, such as the attack paths, methods, and potential target systems. Therefore, prior to developing a risk-informed cyber security strategy, the importance of cyber-attacks and significant critical digital assets (CDAs) should be analyzed. In this work, an importance analysis method for cyber-attacks on an NPP was proposed using the probabilistic safety assessment (PSA) method. To develop an importance analysis framework for cyber-attacks, possible cyber-attacks were identified with failure modes, and a PSA model for cyber-attacks was developed. For case studies, the quantitative evaluations of cyber-attack scenarios were performed using the proposed method. By using quantitative importance of cyber-attacks and identifying significant CDAs that must be defended against cyber-attacks, it is possible to develop an efficient and reliable defense strategy against cyber-attacks on NPPs.

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.66-73
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• 2021

This report documents the results of an at-power internal events Level 1 Probabilistic Safety Assessment (PSA) for a Korea research reactor (KRR). The aim of the study is to determine the accident sequences, construct an internal level 1 PSA model, and estimate the core damage frequency (CDF). The accident quantification is performed using the AIMS-PSA software version 1.2c along with a fault tree reliability evaluation expert (FTREX) quantification engine. The KRR PSA model is quantified using a cut-off value of 1.0E-15/yr to eliminate the non-effective minimal cut sets (MCSs). The final result indicates a point estimate of 4.55E-06/yr for the overall CDF attributable to internal initiating events in the core damage state for the KRR. Loss of Electric Power (LOEP) is the predominant contributor to the total CDF via a single initiating event (3.68E-6/yr), providing 80.9% of the CDF. The second largest contributor is the beam tube loss of coolant accident (LOCA), which accounts for 9.9% (4.49E-07/yr) of the CDF.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.2933-2940
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• 2022

Approximations such as the delete-term approximation, rare event approximation, and minimal cutset upper bound (MCUB) need to be prudently applied for the quantification of a seismic probabilistic safety assessment (PSA) model. Important characteristics of seismic PSA models indicate that preserving the success branches in a primary seismic event tree is necessary. Based on the authors' experience in modeling and quantifying plant-level seismic PSA models, the effects of applying negate-down to the success branches in primary seismic event trees on the quantification results are summarized along with the following three insights gained: (1) there are two competing effects on the MCUB-based quantification results: one tending to increase and the other tending to decrease; (2) the binary decision diagram does not always provide exact quantification results; and (3) it is identified when the exact results will be obtained, and which combination provides more conservative results compared to the others. Complicated interactions occur in Boolean variable manipulation, approximation, and the quantification of a seismic PSA model. The insights presented herein can assist PSA analysts to better understand the important theoretical principles associated with the quantification of seismic PSA models.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.119-130
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• 2023

Among the various elements of probabilistic safety assessment (PSA), human failure events (HFEs) and their dependencies are major contributors to the quantification of risk of a nuclear power plant. Currently, the dependency among HFEs is reflected using a post-processing method in PSA, wherein several drawbacks, such as limited propagation of minimal cutsets through the fault tree and improper truncation of minimal cutsets exist. In this paper, we propose a method to model the HFE dependency directly in a fault tree using the if-then-else logic. The proposed method proved to be equivalent to the conventional post-processing method while addressing the drawbacks of the latter. We also developed a software tool to facilitate the implementation of the proposed method considering the need for modeling the dependency between multiple HFEs. We applied the proposed method to a specific case to demonstrate the drawbacks of the conventional post-processing method and the advantages of the proposed method. When applied appropriately under specific conditions, the direct fault-tree modeling of HFE dependency enhances the accuracy of the risk quantification and facilitates the analysis of minimal cutsets.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1420-1427
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• 2023

Seismic risk has received increased attention since the 2011 Fukushima accident in Japan. The seismic risk of a nuclear power plant is evaluated via seismic probabilistic safety assessment (PSA), for which several methods are available. Recently, the discrete approach has become widely used. This approximates the seismic risk by discretizing the ground motion level interval into a small number of subintervals with the expectation of providing a conservative result. The present study examines the effect of the number of subintervals upon the results of seismic risk quantification. It is demonstrated that a small number of subintervals may lead to either an underestimation or overestimation of the seismic risk depending on the ground motion level. The present paper also provides a method for finding the boundaries between overestimation and underestimation regions, and illustrates the effect of the number of subintervals upon the seismic risk evaluation with an example. By providing a method for determining the effect of a small number of subintervals upon the results of seismic risk quantification, the present study will assist seismic PSA analysts to determine the appropriate number of subintervals and to better understand seismic risk quantification.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2263-2275
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• 2023

In general, an internal fire events probabilistic safety assessment (PSA) model is quantified by modifying the pre-existing internal event PSA model. Because many pieces of equipment or cables can be damaged by a fire, a single fire event can lead to multiple internal events PSA initiating events (IEs). Consequently, when the fire events PSA model is quantified, inappropriate minimal cut sets (MCSs), such as duplicate MCSs, may be generated. This paper shows that single quantification of a hypothetical single-top fire event PSA model may generate the following four types of inappropriate MCSs: duplicate MCSs, MCSs subsumed by other MCSs, nonsense MCSs, and MCSs with over-counted fire frequencies. Among the inappropriate MCSs, the nonsense MCSs should be addressed first because they can interfere with the right interpretation of the other MCSs and prevent the resolution of the issues related to the other inappropriate MCSs. In addition, we propose a resolution process for each of the issues caused by these inappropriate MCSs and suggest an overall procedure for resolving them. The results of this study will contribute to the understanding and resolution of the inappropriate MCSs that may appear in the quantification of fire events PSA models.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2047-2052
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• 2023

Risk-informed approach has been widely applied in the safety design, regulation, and operation of nuclear reactors. It has been commonly accepted that risk-informed design optimization should be used in the innovative reactor designs to make nuclear system highly safe and reliable. In spite of the risk-informed approach has been used in some advanced nuclear reactors designs, such as Westinghouse IRIS, Gen-IV sodium fast reactors and lead-based fast reactors, the process of risk-informed design of nuclear reactors is hardly to carry out when passive system reliability should be integrated in the framework. A practical method for new passive safety reactors based on probabilistic safety assessment (PSA) and passive system reliability analyze linking is proposed in this paper. New three-dimension frequency-consequence curve based on risk concept with three variables is used in this method. The proposed method has been applied to the determination optimization of design options selection in a 10 MWth lead-based research reactor(LR) to obtain one optimized system design in conceptual design stage, using the integrated reliability and probabilistic safety assessment program RiskA, and the computation resources and time consumption in this process was demonstrated reasonable and acceptable.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1520-1526
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• 2014

This paper presents a probabilistic method to evaluate the total transfer capability (TTC) by considering the sequential quadratic programming and the uncertainty of weather conditions. After the initial TTC is calculated by sequential quadratic programming (SQP), the transient stability is checked by time simulation. Also because power systems are exposed to a variety of weather conditions the outage probability is increased due to the weather condition. The probabilistic approach is necessary to evaluate the TTC, and the Monte Carlo Simulation (MCS) is used to accomplish the probabilistic calculation of TTC by considering the various weather conditions.

• Journal of Korea Water Resources Association
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• v.50 no.8
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• pp.563-577
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• 2017

As the uncertainty of precipitation increases due to climate change, seasonal forecasting and the use of weather forecasts become essential for efficient water resources management. In this study, the categorical probabilistic long-term forecasts implemented by KMA (Korea Meteorological Administration) since June 2014 was evaluated using assessment indicators of Hit Rate, Reliability Diagram, and Relative Operating Curve (ROC) and a technique for obtaining quantitative precipitation estimates based on probabilistic forecasts was proposed. The probabilistic long-term forecasts showed its maximum predictability of 48% and the quantified precipitation estimates were closely matched with actual observations; maximum correlation coefficient (R) in predictability evaluation for 100% accurate and actual weather forecasts were 0.98 and 0.71, respectively. A precipitation quantification approach utilizing probabilistic forecasts proposed in this study is expected to enable water management considering the uncertainty of precipitation. This method is also expected to be a useful tool for supporting decision-making in the long-term planning for water resources management and reservoir operations.

• Journal of the Korean Geotechnical Society
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• v.22 no.11
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• pp.101-111
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• 2006

Slope stability analysis is a geotechnical engineering problem characterized by many sources of uncertainty. Some of them are connected to the variability of soil properties involved in the analysis. In this paper, a numerical procedure of probabilistic analysis of slope stability is presented based on Spencer's method of slices. The deterministic analysis is extended to a probabilistic approach that accounts fur the uncertainties and spatial variation of the soil parameters. The procedure is based on the first-order reliability method to compute the Hasofer-Lind reliability index and Monte-Carlo Simulation. A probabilistic stability assessment was performed to obtain the variation of failure probability with the variation of soil parameters in homogeneous and layered slopes as an example. The examples give insight into the application of uncertainty treatment to the slope stability and show the impact of the spatial variability of soil properties on the outcome of a probabilistic assessment.