• Nuclear Engineering and Technology
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• v.46 no.2
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• pp.207-216
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• 2014

This paper revisits the Fukushima accident to draw lessons in the aspect of nuclear safety considering the fact that the Fukushima accident resulted in core damage for three nuclear power plants simultaneously and that there is a high possibility of a failure of the integrity of reactor vessel and primary containment vessel. A brief review on the accident progression at Fukushima nuclear power plants is discussed to highlight the nature and characteristic of the event. As the severe accident management measures at the Fukushima Daiich nuclear power plants seem to be not fully effective, limitations of current severe accident management strategy are discussed to identify the areas for the potential improvements including core cooling strategy, containment venting, hydrogen control, depressurization of primary system, and proper indication of event progression. The gap between the Fukushima accident event progression and current understanding of severe accident phenomenology including the core damage, reactor vessel failure, containment failure, and hydrogen explosion are discussed. Adequacy of current safety goals are also discussed in view of the socio-economic impact of the Fukushima accident. As a conclusion, it is suggested that an investigation on a coherent integrated safety principle for the severe accident and development of innovative mitigation features is necessary for robust and resilient nuclear power system.

• Nuclear Engineering and Technology
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• v.44 no.3
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• pp.311-322
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• 2012

Since the crisis at the Fukushima plants, severe accident progression during a station blackout accident in nuclear power plants is recognized as a very important area for accident management and emergency planning. The purpose of this study is to investigate the comparative characteristics of anticipated severe accident progression among the three typical types of nuclear reactors. A station blackout scenario, where all off-site power is lost and the diesel generators fail, is simulated as an initiating event of a severe accident sequence. In this study a comparative analysis was performed for typical pressurized water reactor (PWR), boiling water reactor (BWR), and pressurized heavy water reactor (PHWR). The study includes the summarization of design differences that would impact severe accident progressions, thermal hydraulic/severe accident phenomenological analysis during a station blackout initiated-severe accident; and an investigation of the core damage process, both within the reactor vessel before it fails and in the containment afterwards, and the resultant impact on the containment.

• Journal of the Korean Society of Safety
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• v.27 no.6
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• pp.192-204
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• 2012

For a severe accident of nuclear power plant, an approach to estimation of the radiological source term using a severe accident code(MELCOR) has been proposed. Although the MELCOR code has a capability to estimate the radiological source term, it has been hardly utilized for the radiological consequence analysis mainly due to a lack of understanding on the relevant function employed in MELCOR and severe accident phenomena. In order to estimate the severe accident source term to be linked with the radiological consequence analysis, this study proposes 4-step procedure: (1) selection of plant condition leading to a severe accident(i.e., accident sequence), (2) analysis of the relevant severe accident code, (3) investigation of the code analysis results and post-processing, and (4) generation of radiological source term information for the consequence analysis. The feasibility study of the present approach to an early containment failure sequence caused by a fast station blackout(SBO) of a reference plant (OPR-1000), showed that while the MELCOR code has an integrated capability for severe accident and source term analysis, it has a large degree of uncertainty in quantifying the radiological source term. Key insights obtained from the present study were: (1) key parameters employed in a typical code for the consequence analysis(i.e., MACCS) could be generated by MELCOR code; (2) the MELOCR code simulation for an assessment of the selected accident sequence has a large degree of uncertainty in determining the accident scenario and severe accident phenomena; and (3) the generation of source term information for the consequence analysis relies on an expert opinion in both areas of severe accident analysis and consequence analysis. Nevertheless, the MELCOR code had a great advantage in estimating the radiological source term such as reflection of the current state of art in the area of severe accident and radiological source term.

• Nuclear Engineering and Technology
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• v.41 no.1
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• pp.63-70
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• 2009

A steam generator tube rupture (SGTR) accident, which is a partial reactor building bypass scenario, has a low probability and high consequences. SAMG has been used to manage the progression of severe accidents and the release of fission products induced by an SGTR at the Wolsong plants. Four of the six SAGs in the SAMG are used to manage the progression of a severe accident induced by an SGTR at the Wolsong plants. The results of the ISAAC code calculation have shown that the proper use the SAMG can stop a severe accident from progressing and keep the reactor building intact during a severe accident. These results confirm that the SAMG is an effective means of managing the progression of severe accidents initiated by an SGTR at the Wolsong plants.

• International Journal of Safety
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• v.2 no.1
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• pp.50-56
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• 2003

There are a lot of uncertainties in the severe accident phenomena and scenarios in nuclear power plants (NPPs) and one of the major issues for severe accident management is the reduction of these uncertainties. The severe accident management aid system using Probabilistic Safety Assessments (PSA) technology is developed for the management staff in order to reduce the uncertainties. The developed system includes the graphical display for plant and equipment status, previous research results by a knowledge-base technique, and the expected plant behavior using PSA. The plant model used in this paper is oriented to identify plant response and vulnerabilities via analyzing the quantified results, and to set up a framework for an accident management program based on these analysis results. Therefore the developed system may playa central role of information source for decision-making for severe accident management, and will be used as a training tool for severe accident management.

• International Journal of Safety
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• v.3 no.1
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• pp.32-37
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• 2004

Recently KAERI has developed the severe accident management guidance to establish Korea standard severe accident management system. On the other hand the PC-based severe accident training simulator SATS has been developed, and the MELCOR code is used as the simulation engine. SATS graphically displays and simulates the severe accidents with interactive user commands. The control capability of SATS could make a severe accident training course more interesting and effective. In this paper the development and functions of the electrical hypertext guidance module HyperKAMG and the SATS-HyperKAMG linkage system for the severe accident management are described.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.393-404
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• 2012

After the Fukushima nuclear accident in 2011, there has been increasing concern regarding severe accidents in nuclear facilities. Severe accident scenarios are difficult for operators to monitor and identify. Therefore, accurate prediction of a severe accident is important in order to manage it appropriately in the unfavorable conditions. In this study, artificial intelligence (AI) techniques, such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH), and fuzzy neural network (FNN), were used to monitor the major transient scenarios of a severe accident caused by three different initiating events, the hot-leg loss of coolant accident (LOCA), the cold-leg LOCA, and the steam generator tube rupture in pressurized water reactors (PWRs). The SVC and PNN models were used for the event classification. The GMDH and FNN models were employed to accurately predict the important timing representing severe accident scenarios. In addition, in order to verify the proposed algorithm, data from a number of numerical simulations were required in order to train the AI techniques due to the shortage of real LOCA data. The data was acquired by performing simulations using the MAAP4 code. The prediction accuracy of the three types of initiating events was sufficiently high to predict severe accident scenarios. Therefore, the AI techniques can be applied successfully in the identification and monitoring of severe accident scenarios in real PWRs.

• Nuclear Engineering and Technology
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• v.47 no.1
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• pp.74-84
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• 2015

If a transient occurs in a nuclear power plant (NPP), operators will try to protect the NPP by estimating the kind of abnormality and mitigating it based on recommended procedures. Similarly, operators take actions based on severe accident management guidelines when there is the possibility of a severe accident occurrence in an NPP. In any such situation, information about the occurrence time of severe accident-related events can be very important to operators to set up severe accident management strategies. Therefore, support systems that can quickly provide this kind of information will be very useful when operators try to manage severe accidents. In this research, the occurrence times of several events that could happen during a severe accident were predicted using support vector machines with short time variations of plant status variables inputs. For the preliminary step, the break location and size of a loss of coolant accident (LOCA) were identified. Training and testing data sets were obtained using the MAAP5 code. The results show that the proposed algorithm can correctly classify the break location of the LOCA and can estimate the break size of the LOCA very accurately. In addition, the occurrence times of severe accident major events were predicted under various severe accident paths, with reasonable error. With these results, it is expected that it will be possible to apply the proposed algorithm to real NPPs because the algorithm uses only the early phase data after the reactor SCRAM, which can be obtained accurately for accident simulations.

• Journal of the Korean Society of Safety
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• v.29 no.5
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• pp.160-165
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• 2014

The realistic cases causing severe core damage should be analyzed and arranged systematically for preparing an accident management of the specific nuclear power plant. The objective of this paper is to establish basic technical information for reactor safety and reactor building integrity management strategies in CANDU reactor severe accident. For the development of severe accident management strategies, plant specific features and behaviors must be studied by detailed analysis works. This analysis scope will serve to cover overall methods and analyzing results to understand the reactor building integrity status in the most likely severe accident sequences that could occur at CANDU reactor. Also analysis results could help prevent or mitigate severe accidents for the identification of any plant specific vulnerabilities to severe accidents using the probabilistic safety assessment (PSA) quantified results.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.95-103
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• 2022

Quick and accurate understanding of the situation in a severe accident is essential for conducting the appropriate accident management and response using the accident diagnosis information. This study employed deep learning technology to diagnose severe accidents through the major safety parameters transferred from a nuclear power plant (NPP) to AtomCARE. After selecting the major accident scenarios to consider, a learning database was established for particular scenarios affiliated with major scenarios by performing a large number of severe accident analyses using MAAP5 code. The severe accident diagnosis technology, which classifies detailed accident scenarios using the major safety parameters from NPPs, was developed by training it with the established database . Verification and validation were conducted by blind test and principal component analysis. The technology developed in this study is expected to be extended and applied to all severe accident scenarios and be utilized as a base technology for quick and accurate severe accident diagnosis.