• Journal of Radiation Protection and Research
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• 제41권1호
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• pp.7-14
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• 2016

연구배경: 본 연구는 일본 후쿠시마 원전사고 이후 원전사고 및 방사능 오염에 대한 우리 국민들의 인식도를 확인하기 위한 목적으로 진행되었다. 재료 및 방법: 설문조사는 수도권에 거주하는 성인 600명을 대상으로 하였다. 결과 및 논의: 연구 결과, 대다수 국민들은 일본 원전사고로 인한 방사능 누출이 우리나라에 영향을 미치고 있다고 우려하였다. 식품, 특히 수산물의 방사능 오염에 대한 우려가 높았고, 선호하는 정보 취득원은 TV(49.8%)와 인터넷 매체(31.3%)였다. 한편 다수의 국민들은 일본 원전사고 및 방사능 오염 정보가 충분히 제공되지 않았고, 원전사고 이후 우리 정부의 대응조치를 잘 모르고 있는 것으로 나타났다. 또한 국민 대다수가 식품 환경 등의 방사능 오염 정도와 안전성에 대한 정보제공 강화가 가장 중요한 것으로 인식하고 있었다. 결론: 본 연구 결과는 향후 방사능에 대한 올바른 인식 확립과 방사능 안전사고 리스크커뮤니케이션을 위한 유용한 자료로 활용될 수 있을 것으로 기대된다.

• Nuclear Engineering and Technology
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• 제28권6호
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• pp.507-521
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• 1996

Quantification of uncertainties in the source term estimations by a large computer code, such as MELCOR and MAAP, is an essential process of the current Probabilistic safety assessment. The main objective of the present study is to investigate the applicability of a combined procedure of the response surface method (RSM) based on input determined from a statistical design and the Latin hypercube sampling (LHS) technique for the uncertainty analysis of CsI release fractions under a Hypothetical severe accident sequence of a station blackout at Younggwang nuclear power plant using MAAP3. OB code as a benchmark problem. On the basis of the results obtained in the present work, the RSM is recommended to be used as a principal tool for an overall uncertainty analysis in source term quantifications, while using the LHS in the calculations of standardized regression coefficients (SRC) and standardized rank regression coefficient (SRRC) to determine the subset of the most important input parameters in the final screening step and to check the cumulative distribution functions obtained by RSM. Verification of the response surface model for its sufficient accuracy is a prerequisite for the reliability of the final results that can be obtained by the combined procedure proposed in the present work.

• Nuclear Engineering and Technology
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• 제55권5호
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• pp.1630-1643
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• 2023

The correct situation awareness (SA) of operators is important for managing nuclear power plants (NPPs), particularly in accident-related situations. Among the three levels of SA suggested by Ensley, Level 3 SA (i.e., projection of the future status of the situation) is challenging because of the complexity of NPPs as well as the uncertainty of accidents. Hence, several prediction methods using artificial intelligence techniques have been proposed to assist operators in accident prediction. However, these methods only predict short-term plant status (e.g., the status after a few minutes) and do not provide information regarding the uncertainty associated with the prediction. This paper proposes an algorithm that can predict the multivariate and long-term behavior of plant parameters for 2 h with 120 steps and provide the uncertainty of the prediction. The algorithm applies bidirectional long short-term memory and an attention mechanism, which enable the algorithm to predict the precise long-term trends of the parameters with high prediction accuracy. A conditional variational autoencoder was used to provide uncertainty information about the network prediction. The algorithm was trained, optimized, and validated using a compact nuclear simulator for a Westinghouse 900 MWe NPP.

• Nuclear Engineering and Technology
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• 제45권4호
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• pp.459-468
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• 2013

During a station blackout (SBO), the initiating event is a loss of Class IV and Class III power, causing the loss of the pumps, used in systems such as the primary heat transporting system (PHTS), moderator cooling, shield cooling, steam generator feed water, and re-circulating cooling water. The reference case of the SBO case does not credit any of these active heat sinks, but only relies on the passive heat sinks, particularly the initial water inventories of the PHTS, moderator, steam generator secondary side, end shields, and reactor vault. The reference analysis is followed by a series of sensitivity cases assuming certain system availabilities, in order to assess their mitigating effects. This paper also establishes the strategies to mitigate SBO accidents. Current studies and strategies use the computer code of the Integrated Severe Accident Analysis Code (ISAAC) for Wolsong plants. The analysis results demonstrate that appropriate strategies to mitigate SBO accidents are established and, in addition, the symptoms of the SBO processes are understood.

• Nuclear Engineering and Technology
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• 제56권6호
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• pp.1989-2001
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• 2024

Artificial intelligence (AI) techniques are now being considered in the nuclear field, but application faces with the lack of actual plant data. For this reason, most previous studies on AI applications in nuclear power plants (NPPs) have relied on simulators or thermal-hydraulic codes to mimic the plants. However, it remains uncertain whether an AI model trained using a simulator can properly work in an actual NPP. To address this issue, this study suggests the use of metadata, which can give information about parameter trends. Referred to here as robust AI, this concept started with the idea that although the absolute value of a plant parameter differs between a simulator and actual NPP, the parameter trend is identical under the same scenario. Based on the proposed robust AI, this study designs an event diagnosis algorithm to classify abnormal and emergency scenarios in NPPs using prototypical learning. The algorithm was trained using a simulator referencing a Westinghouse 990 MWe reactor and then tested in different environments in Advanced Power Reactor 1400 MWe simulators. The algorithm demonstrated robustness with 100 % diagnostic accuracy (117 out of 117 scenarios). This indicates the potential of the robust AI-based algorithm to be used in actual plants.

• Nuclear Engineering and Technology
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• 제52권1호
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• pp.69-77
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• 2020

After the Fukushima Daiichi accidents, public concerns on nuclear safety and the corresponding burden of nuclear power plant licensees are increasing. In order to secure public trust and enhance the rationality of current safety regulation, we develop a risk-informed decision making (RIDM) framework for the Korean regulatory body. By analyzing all the regulatory activities for nuclear power plants in Korea, eight action items are selected for RIDM implementation, with appropriate procedures developed for each. For two items in particular - the accident sequence precursor analysis (ASPA) and the significance determination process (SDP) - two customized risk evaluation software has been developed for field inspectors and probabilistic safety assessment experts, respectively. The effectiveness of the proposed RIDM framework is demonstrated by applying the ASPA procedure to 35 unplanned scrams and the SDP to 24 findings from periodic inspections.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2015년도 춘계 종합학술대회 논문집
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• pp.439-440
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• 2015

There has been a big need of Safety Education for the power plants with a high risk due to the Fukushima Daiichi nuclear disaster and the tragic accident of Sewol Ferry. The object of this research is for studying ways of developing contents for customized Power Plants Safety Education applied with 'Story Viewing' technology in order to improve the present format of Power Plant Safety Education based on hard copied documents so as to prevent human mistakes because of lack of system and ability of initial response which come from safety frigidity shown in the case of Sewol Accident. 'Story-viewing' applied to Power Plant Safety Education is the methodology to enhance information communicability utilizing IT/Visualization technology combined with Story Telling that is an effective propagation way.

• Nuclear Engineering and Technology
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• 제53권4호
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• pp.1181-1188
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• 2021

When abnormal events occur in a nuclear power plant, operators must conduct appropriate abnormal operating procedures. It is burdensome though for operators to choose the appropriate procedure considering the numerous main plant parameters and hundreds of alarms that should be judged in a short time. Recently, various research has applied deep-learning algorithms to support this problem by classifying each abnormal condition with high accuracy. Most of these models are trained with simulator data because of a lack of plant data for abnormal states, and as such, developed models may not have tolerance for plant data in actual situations. In this study, two approaches are investigated for a deep-learning model trained with simulator data to overcome the performance degradation caused by noise in actual plant data. First, a preprocessing method using several filters was employed to smooth the test data noise, and second, a data augmentation method was applied to increase the acceptability of the untrained data. Results of this study confirm that the combination of these two approaches can enable high model performance even in the presence of noisy data as in real plants.

• Nuclear Engineering and Technology
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• 제51권3호
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• pp.702-708
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• 2019

This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches, requiring time-integrated parameters and independent firing algorithms. NARX neural network is able to directly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The case studied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number of hidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performing parametric studies to determine the network architecture with minimum error. The developed NARX neural network is trained by error back propagation algorithm with different break sizes, covering 5% -100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5 thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neural network for break size estimation in NPPs. It is able to find a general solution for break size estimation problem in real-time, using a limited number of training data sets. This study has been performed in the framework of a research project, aiming to develop an appropriate accident management support tool for Bushehr NPP.

• Nuclear Engineering and Technology
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• 제54권8호
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• pp.2960-2973
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• 2022

Since Fukushima nuclear power plant (NPP) accident in 2011, the importance of research on various severe accident phenomena has been emphasized. Particularly, detailed analysis of combustion risk is necessary following the containment damage caused by combustion in the Fukushima accident. Many studies have been conducted to evaluate the risk of local hydrogen concentration increases and flame propagation using computational code. In particular, the potential for combustion by local hydrogen concentration in specific areas within the containment has been emphasized. In this study, the process of flame propagation generated inside a reactor core to containment during a loss of coolant accident (LOCA) was analyzed using MELCOR 2.1 code. Later in the LOCA scenario, it was expected that hydrogen combustion occurred inside the reactor core owing to oxygen inflow through the cold leg break area. The main driving force of the oxygen intrusion is the elevated containment pressure due to the molten corium-concrete interaction. The thermal and mechanical loads caused by the flame threaten the integrity of the containment. Additionally, the containment spray system effectiveness in this situation was evaluated because changes in pressure gradient and concentrations of flammable gases greatly affect the overall behavior of ignition and subsequent containment integrity.