• Title, Summary, Keyword: Nuclear Accident

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Smart support system for diagnosing severe accidents in nuclear power plants

  • Yoo, Kwae Hwan;Back, Ju Hyun;Na, Man Gyun;Hur, Seop;Kim, Hyeonmin
    • Nuclear Engineering and Technology
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    • v.50 no.4
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    • pp.562-569
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    • 2018
  • Recently, human errors have very rarely occurred during power generation at nuclear power plants. For this reason, many countries are conducting research on smart support systems of nuclear power plants. Smart support systems can help with operator decisions in severe accident occurrences. In this study, a smart support system was developed by integrating accident prediction functions from previous research and enhancing their prediction capability. Through this system, operators can predict accident scenarios, accident locations, and accident information in advance. In addition, it is possible to decide on the integrity of instruments and predict the life of instruments. The data were obtained using Modular Accident Analysis Program code to simulate severe accident scenarios for the Optimized Power Reactor 1000. The prediction of the accident scenario, accident location, and accident information was conducted using artificial intelligence methods.

SEVERE ACCIDENT ISSUES RAISED BY THE FUKUSHIMA ACCIDENT AND IMPROVEMENTS SUGGESTED

  • Song, Jin Ho;Kim, Tae Woon
    • 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.

A Preliminary Study for the Implementation of General Accident Management Strategies

  • Yang, Soo-Hyung;Kim, Soo-Hyung;Jeong, Young-Hoon;Chang, Soon-Heung
    • Proceedings of the Korean Nuclear Society Conference
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    • pp.695-700
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    • 1997
  • To enhance the safety of nuclear power plants, implementation of accident management has been suggested as one of most important programs. Specially, accident management strategies are suggested as one of key elements considered in development of the accident management program. In this study, generally applicable accident management strategies to domestic nuclear power plants are identified through reviewing several accident management programs for the other countries and considering domestic conditions. Identified strategies are as follows; 1) Injection into the Reactor Coolant System, 2) Depressurize the Reactor Coolant System, 3) Depressurize the Steam Generator, 4) Injection into the Steam Generator, 5) Injection into the Containment, 6) Spray into the Containment, 7) Control Hydrogen in the Containment. In addition, the systems and instrumentation necessary for the implementation of .each strategy are also investigated.

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MONITORING SEVERE ACCIDENTS USING AI TECHNIQUES

  • No, Young-Gyu;Kim, Ju-Hyun;Na, Man-Gyun;Lim, Dong-Hyuk;Ahn, Kwang-Il
    • 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.

An accident diagnosis algorithm using long short-term memory

  • Yang, Jaemin;Kim, Jonghyun
    • Nuclear Engineering and Technology
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    • v.50 no.4
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    • pp.582-588
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    • 2018
  • Accident diagnosis is one of the complex tasks for nuclear power plant (NPP) operators. In abnormal or emergency situations, the diagnostic activity of the NPP states is burdensome though necessary. Numerous computer-based methods and operator support systems have been suggested to address this problem. Among them, the recurrent neural network (RNN) has performed well at analyzing time series data. This study proposes an algorithm for accident diagnosis using long short-term memory (LSTM), which is a kind of RNN, which improves the limitation for time reflection. The algorithm consists of preprocessing, the LSTM network, and postprocessing. In the LSTM-based algorithm, preprocessed input variables are calculated to output the accident diagnosis results. The outputs are also postprocessed using softmax to determine the ranking of accident diagnosis results with probabilities. This algorithm was trained using a compact nuclear simulator for several accidents: a loss of coolant accident, a steam generator tube rupture, and a main steam line break. The trained algorithm was also tested to demonstrate the feasibility of diagnosing NPP accidents.

Trends in Risk Management and Accident Management in Nuclear Industry

  • Kim, Inn-Seock
    • Proceedings of the Korean Nuclear Society Conference
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    • pp.481-486
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    • 1996
  • Safety management may be classified into three dimensions: (1) risk management, (2) accident management, and (3) emergency management. This paper addresses the recent trends of safety management in nuclear industry, focussing on risk management and accident management.

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COMPARATIVE ANALYSIS OF STATION BLACKOUT ACCIDENT PROGRESSION IN TYPICAL PWR, BWR, AND PHWR

  • Park, Soo-Yong;Ahn, Kwang-Il
    • 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.

A Study on the Change of Nuclear Power Plant News Frame in Korean Newspapers Before and After Fukushima Nuclear Accident in Japan (우리나라 원전에 대한 신문 보도 프레임 변화 연구 일본 후쿠시마 원전 사고 전후 비교)

  • Shim, Eun-Jung;Kim, Wi-Geun
    • Korean journal of communication and information
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    • v.76
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    • pp.124-150
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    • 2016
  • The aim of this study is to see the change of the general characteristics and frame of nuclear power plant news in Korea from comparing the before Fukushima nuclear accident in Japan on March 11, 2011 with the after. To this aim, the national daily newspapers and the local daily newspapers in Busan located nuclear power plants were selected, and the content analysis of the newspaper stories about nuclear power plants was done. In research results, the stories about nuclear power plants in Korean newspapers increased greatly after Fukushima nuclear accident. Before the accident the nuclear power plant stories about economy held a large majority, while after the accident the stories about society held. Fukushima nuclear accident served as the momentum that the nuclear power plant stories in Korea became main news. Meanwhile, the frame of nuclear power plant stories in Korean newspapers changed greatly after the accident. Justly the environmental security frame increased greatly, because of increasing greatly the stories about security of nuclear power plants with Fukushima nuclear accident. Particularly in the local daily newspapers in Busan before the accident the environmental security frame was 29.3% of stories about nuclear power plants, and after the accident the frame was 77.6%.

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Development of an Accident Sequence Precursor Methodology and its Application to Significant Accident Precursors

  • Jang, Seunghyun;Park, Sunghyun;Jae, Moosung
    • Nuclear Engineering and Technology
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    • v.49 no.2
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    • pp.313-326
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    • 2017
  • The systematic management of plant risk is crucial for enhancing the safety of nuclear power plants and for designing new nuclear power plants. Accident sequence precursor (ASP) analysis may be able to provide risk significance of operational experience by using probabilistic risk assessment to evaluate an operational event quantitatively in terms of its impact on core damage. In this study, an ASP methodology for two operation mode, full power and low power/shutdown operation, has been developed and applied to significant accident precursors that may occur during the operation of nuclear power plants. Two operational events, loss of feedwater and steam generator tube rupture, are identified as ASPs. Therefore, the ASP methodology developed in this study may contribute to identifying plant risk significance as well as to enhancing the safety of nuclear power plants by applying this methodology systematically.

Reactor Vessel Water Level Estimation During Severe Accidents Using Cascaded Fuzzy Neural Networks

  • Kim, Dong Yeong;Yoo, Kwae Hwan;Choi, Geon Pil;Back, Ju Hyun;Na, Man Gyun
    • Nuclear Engineering and Technology
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    • v.48 no.3
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    • pp.702-710
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    • 2016
  • Global concern and interest in the safety of nuclear power plants have increased considerably since the Fukushima accident. In the event of a severe accident, the reactor vessel water level cannot be measured. The reactor vessel water level has a direct impact on confirming the safety of reactor core cooling. However, in the event of a severe accident, it may be possible to estimate the reactor vessel water level by employing other information. The cascaded fuzzy neural network (CFNN) model can be used to estimate the reactor vessel water level through the process of repeatedly adding fuzzy neural networks. The developed CFNN model was found to be sufficiently accurate for estimating the reactor vessel water level when the sensor performance had deteriorated. Therefore, the developed CFNN model can help provide effective information to operators in the event of a severe accident.