• Nuclear Engineering and Technology
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• v.48 no.3
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• pp.684-701
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• 2016

The Gaussian process model (GPM) is a flexible surrogate model that can be used for nonparametric regression for multivariate problems. A unique feature of the GPM is that a prediction variance is automatically provided with the regression function. In this paper, we estimate the safety margin of a nuclear power plant by performing regression on the output of best-estimate simulations of a large-break loss-of-coolant accident with sampling of safety system configuration, sequence timing, technical specifications, and thermal hydraulic parameter uncertainties. The key aspect of our approach is that the GPM regression is only performed on the dominant input variables, the safety injection flow rate and the delay time for AC powered pumps to start representing sequence timing uncertainty, providing a predictive model for the peak clad temperature during a reflood phase. Other uncertainties are interpreted as contributors to the measurement noise of the code output and are implicitly treated in the GPM in the noise variance term, providing local uncertainty bounds for the peak clad temperature. We discuss the applicability of the foregoing method to reduce the use of conservative assumptions in best estimate plus uncertainty (BEPU) and Level 1 probabilistic safety assessment (PSA) success criteria definitions while dealing with a large number of uncertainties.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3298-3313
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• 2021

The objective of this study is to estimate the occurrence probabilities of severe accident phenomena based on a fuzzy elicitation technique. Normally, it is difficult to determine these probabilities due to the lack of information on severe accident progression and the highly uncertain values currently in use. In this case, fuzzy set theory (FST) can be best exploited. First, questions were devised for expert elicitation on technical issues of severe accident phenomena. To deal with ambiguities and the imprecision of previously developed (reference) probabilities, fuzzy aggregation methods based on FST were employed to derive the occurrence probabilities of severe accidents via four phases: 1) choosing experts, 2) quantifying weighting factors for the experts, 3) aggregating the experts' opinions, and 4) defuzzifying the fuzzy numbers. In this way, this study obtained expert elicitation results in the form of updated occurrence probabilities of severe accident phenomena in the OPR-1000 plant, after which the differences between the reference probabilities and the newly acquired probabilities using fuzzy aggregation were compared, with the advantages of the fuzzy technique over other approaches explained. Lastly, the impact of applying the updated severe accident probabilities on containment integrity was quantitatively investigated in a Level 2 PSA model.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.644-654
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• 2024

After the Tohoku earthquake and tsunami (Japan, 2011), regulatory efforts to mitigate external hazards have increased both the safety requirements and the total capital cost of nuclear power plants (NPPs). In these circumstances, identifying not only disaster robustness but also cost-effective capacity setting of NPPs has become one of the most important tasks for the nuclear power industry. A few studies have been performed to relocate the seismic capacity of NPPs, yet the effects of multiple hazards have not been accounted for in NPP capacity optimization. The major challenges in extending this problem to the multihazard dimension are (1) the high computational costs for both multihazard risk quantification and system-level optimization and (2) the lack of capital cost databases of NPPs. To resolve these issues, this paper proposes an effective method that identifies the optimal multihazard capacity of NPPs using a multi-objective genetic algorithm and the two-stage direct quantification of fault trees using Monte Carlo simulation method, called the two-stage DQFM. Also, a capacity-based indirect capital cost measure is proposed. Such a proposed method enables NPP to achieve safety and cost-effectiveness against multi-hazard simultaneously within the computationally efficient platform. The proposed multihazard capacity optimization framework is demonstrated and tested with an earthquake-tsunami example.

• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.87-96
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• 2020

The purpose of this paper is to develop a fire HRA (Human Reliability Analysis) procedure for full power operation of domestic NPPs (Nuclear Power Plants). For the development of fire HRA procedure, the recent research results of NUREG-1921 in an effort to meet the requirements of the ASME/ANS PRA Standard were reviewed. The K-HRA method, a standard method for HRA of a domestic level 1 PSA (Probabilistic Safety Assessment) and fire related procedures in domestic NPPs were reviewed. Based on the review, a procedure for the fire HRA required for a domestic fire PSA based on the K-HRA method was developed. To this end, HRA issues such as new operator actions required in the event of a fire and complexity of fire situations were considered. Based on the four kinds of HFE (Human Failure Event) developed for a fire HRA in this research, a qualitative analysis such as feasibility evaluation was suggested. And also a quantitative analysis process which consists of screening analysis and detailed analysis was proposed. For the qualitative analysis, a screening analysis by NUREG-1921 was used. In this research, the screening criteria for the screening analysis was modified to reduce vague description and to reflect recent experimental results. For a detailed analysis, the K-HRA method and scoping analysis by NUREG-1921 were adopted. To apply K-HRA to fire HRA for quantification, efforts to modify PSFs (Performance Shaping Factors) of K-HRA to reflect fire situation and effects were made. For example, an absence of STA (Shift Technical Advisor) to command a fire brigade at a fire area is considered and the absence time should be reflected for a HEP (Human Error Probability) quantification. Based on the fire HRA procedure developed in this paper, a case study for HEP quantification such as a screening analysis and detailed analysis with the modified K-HRA was performed. It is expected that the HRA procedure suggested in this paper will be utilized for fire PSA for domestic NPPs as it is the first attempt to establish an HRA process considering fire effects.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.526-535
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• 2024

This paper proposes a safety-critical standby component unavailability model that contains aging effects caused by the elapsed time from installation, component degradation due to surveillance tests, and imperfect maintenance actions. An application of the model to a Motor-Operated Valve and a Motor-Driven Pump involved in the HPIS of a VVER/1000-V446 nuclear power plant is demonstrated and compared with other existing models at component and system levels. In addition, the effects of different unavailability models are reflected in the NPP's risk criterion, i.e., core damage frequency, over five maintenance periods. The results show that, compared with other models that do not simultaneously consider the full effects of degradation and maintenance impacts, the proposed model realistically evaluates the unavailabilities of the safety-related components and the involved systems as a plant age function. Therefore, it can effectively reflect the age-dependent CDF impact of a given testing and maintenance policy in a specified time horizon.

• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.107-115
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• 2020

This paper introduces the first vital area identification (VAI) process for the physical protection of nuclear power plants (NPPs) during low power and shutdown (LPSD) operation. This LPSD VAI is based on the 3rd generation VAI method which very efficiently utilizes probabilistic safety assessment (PSA) event trees (ETs). This LPSD VAI process was implemented to the virtual NPP during LPSD operation in this study. Korea Atomic Energy Research Institute (KAERI) had developed the 2nd generation full power VAI method that utilizes whole internal and external (fire and flooding) PSA results of NPPs during full power operation. In order to minimize the huge burden of the 2nd generation full power VAI method, the 3rd generation full power VAI method was developed, which utilizes ETs and minimal PSA fault trees instead of using the whole PSA fault tree. In the 3rd generation full power VAI method, (1) PSA ETs are analyzed, (2) minimal mitigation systems for avoiding core damage are selected from ETs by calculating system-level target sets and prevention sets, (3) relatively small sabotage fault tree that has the systems in the shortest system-level prevention set is composed, (4) room-level target sets and prevention sets are calculated from this small sabotage fault tree, and (5) the rooms in the shortest prevention set are defined as vital areas that should be protected. Currently, the 3rd generation full power VAI method is being employed for the VAI of Korean NPPs. This study is the first development and application of the 3rd generation VAI method to the LPSD VAI of NPP. For the LPSD VAI, (1) many LPSD ETs are classified into a few representative LPSD ETs based on the functional similarity of accident scenarios, (2) a few representative LPSD ETs are simplified with some VAI rules, and then (3) the 3rd generation VAI is performed as mentioned in the previous paragraph. It is well known that the shortest room-level prevention sets that are calculated by the 2nd and 3rd generation VAI methods are identical.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.899-905
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• 1998

The analysis priority makings the recommendation to reduce the total core damage frequency (CDF) of Wolsong nuclear Power Plant nits 2/3/4 was Performed in this paper. In order to derive the recommendation, the sensitivity analysis of CDF on which major contributors effect m performed based on the accident quantification results during Level 1 Probabilistic safety assessment (PSA). Priorities were ranked in tile way that compares the CDF reduction rate with efforts required to implement those recommendations using risk matrix

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.3
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• pp.43-59
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• 1995

This paper presents a method for estimating the population variability distribution of the failure parameter (failure rate or failure probability) for each failure mode considered in PSA (Probabilistic Safety Assessment). We focus on the utilization of generic estimates from various industry compendia for the estimation. The estimates are complicated statistics of failure data from plants. When the failure data referred in two or more sources are overlapped, dependency occurs among the estimates provided by the sources. This type of problem is first addressed in this paper. We propose methods based on ML-II estimation in Bayesian framework and discuss the characteristics of the proposed estimators. The proposed methods are easy to apply in real field. Numerical examples are also provided.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.368-375
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• 2021

The performance time of human operators has been recognized as a key aspect of human reliability in socio-complex systems, including nuclear industries. Because of the importance of the time factor, most existing human reliability assessment methods provide ways to quantify human error probabilities (HEPs) that are associated with the performance time. To quantify such kinds of HEPs, it is crucial to rationally predict the length of time required and time available and compare them. However, there have not been detailed guidelines that identify the critical cue presentation time or initial time of human performance, which is important to calculate the time information. In this paper, we introduce a time-related HEP calculation technique with a decision algorithm that determines the critical cue and its timing. The calculation process is presented with the application examples. It is expected that the proposed algorithm will reduce the variability in the time-related reliability assessment and strengthen the scientific evidence of the assessment process. The detailed description is provided in the technical report KAERI/TR-7607/2019.

• Journal of the Korean Society of Safety
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• v.27 no.5
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• pp.219-223
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• 2012

For an off-site consequence analysis at nuclear power plant, MELCOR Accident Consequence Code System(MACCS) II code is widely used as a software tool. In this study, the algorithm of web-based off-site consequence analysis program(OSCAP) using the MACCS II code was developed for an Integrated Leak Rate Test (ILRT) interval extension and Level 3 probabilistic safety assessment(PSA), and verification and validation(V&V) of the program was performed. The main input data for the MACCS II code are meteorological, population distribution and source term information. However, it requires lots of time and efforts to generate the main input data for an off-site consequence analysis using the MACCS II code. For example, the meteorological data are collected from each nuclear power site in real time, but the formats of the raw data collected are different from each site. To reduce the efforts and time for risk assessments, the web-based OSCAP has an automatic processing module which converts the format of the raw data collected from each site to the input data format of the MACCS II code. The program also provides an automatic function of converting the latest population data from Statistics Korea, the National Statistical Office, to the population distribution input data format of the MACCS II code. For the source term data, the program includes the release fraction of each source term category resulting from modular accident analysis program(MAAP) code analysis and the core inventory data from ORIGEN. These analysis results of each plant in Korea are stored in a database module of the web-based OSCAP, so the user can select the defaulted source term data of each plant without handling source term input data.