• Nuclear Engineering and Technology
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• 제50권8호
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• pp.1246-1254
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• 2018

The importance of performing Level 3 probabilistic safety assessments (PSA) along with a general interest in assessing multi-unit risk has been sharply increasing after the Fukushima Daiichi nuclear power plant (NPP) accident. However, relatively few studies on multi-unit Level 3 PSA have been performed to date, reflecting limited scenarios of multi-unit accidents with higher priority. The major difficulty to carry out a multi-unit Level 3 PSA lies in the exponentially increasing number of multi-unit accident combinations, as different source terms can be released from each NPP unit; indeed, building consequence models for the astronomical number of accident scenarios is simply impractical. In this study, a new approach has been developed that employs the look-up table method to cover every multi-unit accident scenario. Consequence results for each scenario can be found on the table, established with a practical amount of effort, and can be matched to the frequency of the scenario. Preliminary application to a six-unit NPP site was carried out, where it was found that the difference between full-coverage and cut-off cases could be considerably high and therefore influence the total risk. Additional studies should be performed to fine tune the details and overcome the limitations of the approach.

• 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.

• Nuclear Engineering and Technology
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• 제53권8호
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• pp.2534-2546
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• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.

• Journal of Radiation Protection and Research
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• 제45권1호
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• pp.2-10
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• 2020

Background: Huge amounts of radionuclides were released into the environment due to the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident, which caused not only serious contamination on the ground, but also radiation exposure to the public. One problem that remains in performing the dose estimation is the difficulty of estimating the internal thyroid dose due to the intake of radioiodine (mainly, ¹³¹I) because of limitations to the human data available. Materials and Methods: The relevant papers were collected and reviewed by the authors. The results of thyroid dose estimates from different studies were tabulated for comparison. Results and Discussion: The thyroid dose estimates from the studies varied widely. The dose estimates by the United Nations Scientific Committee on the Effects of Atomic Radiation were higher than the others due to the ingestion dose being based on conservative assumptions. The dose estimates by Japanese experts were mostly below 20-30 mSv. The recent studies suggested that exposure on March 12, 2011 would be crucial for late evacuees from the areas near the FD-NPP because of the possible intake of short-lived radionuclides other than ¹³¹I. Further multilateral studies are vital to reduce uncertainties in the present dose estimations. Conclusion: The estimation of the thyroid doses to Fukushima residents still has many uncertainties. However, it is considered unlikely that the thyroid doses exceeded 50 mSv except in some extreme cases. Further multilateral studies are thus necessary to reduce the uncertainties in the present dose estimations.

• 시스템엔지니어링학술지
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• 제15권1호
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• pp.51-59
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• 2019

After Fukushima Dai-ichi NPP accident, the need for implementation of diverse and flexible coping strategies (FLEX) became evident. However, to ensure the effectiveness of the safety strategy, it is essential to quantify the uncertainties associated with the station blackout (SBO) scenario as well as the operator actions. In this paper, a systems engineering approach for uncertainty analysis (UA) of a SBO scenario in advanced pressurized water reactor is performed. MARS-KS is used as a best estimate thermal-hydraulic code and is loosely-coupled with Dakota software which is employed to develop the uncertainty quantification framework. Furthermore, the systems engineering approach is adopted to identify the requirements, functions and physical architecture, and to develop the verification and validation plan. For the preliminary analysis, 13 uncertainty parameters are propagated through the model to evaluate the stability and convergence of the framework. The developed framework will ultimately be used to quantify the aleatory and epistemic uncertainties associated with an extended SBO accident scenario and assess the coping capability of APR1400 and the effectiveness of the implemented FLEX strategies.

• 한국자원식물학회:학술대회논문집
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• 한국자원식물학회 2018년도 추계학술대회
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• pp.25-25
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• 2018

The Fukushima Daiichi Nuclear Power Plant accident in March 2011 caused severe radioactive contamination in the surrounding environment. Since the accident, much attention has been paid to the biological and genetic consequences of organism inhabiting the contaminated area. The effect of radiation exposure on genetic mutation rates is little known, especially for low doses and in situ conditions. Evaluating DNA mutation by low levels of radiation dose is difficult due to the rare mutation event and lack of sequence information before the accident. In this study, correlations with air dose levels and somatic DNA mutation rates were evaluated using Next Generation Sequencer for the clonal plant, Phyllostachys edulis. This bamboo is known to spread an identical clone throughout Japan, and it has the advantage that we can compare genetic mutation rate among identical clone growing different air dose levels. We collected 94 samples of P. edulis from 14 sites with air dose rates from $0.04{\sim}7.80{\mu}Gy/h$. Their clonal identity was confirmed by analysis using 24 microsatellite markers, and then, sequences among samples were compared by MIG sequence. The sequence data were obtained from 2,718 loci. About ~200,000 bp sequence (80 bp X 2,718 loci) were obtained for each sample, and this corresponds to about 0.01% of the genome sequence of P. edulis. In these sequences, 442 loci showed polymorphism patterns including recent origin mutation, old mutation, and sequence errors. The number of mutations per sample ranged from 0 to 13, and did not correlate with air dose levels. This result indicated that DNA mutations have not accumulated in P. edulis living in the air doses levels less than $10{\mu}Gy/h$. Our study also suggests that mutation rates can be assessed by selecting an appropriate experimental approach and analyzing with next generation sequencer.

• Nuclear Engineering and Technology
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• 제52권10호
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• pp.2221-2229
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• 2020

After the Fukushima Daiichi nuclear power plant (NPP) accident, level 3 probabilistic safety assessment (PSA) has emerged as an important task in order to assess the risk level of the multi-unit NPPs in a single nuclear site. Accurate calculation of the radionuclide concentrations and exposure doses to the public is required if a nuclear site has multi-unit NPPs and large number of people live near NPPs. So, there has been a great need to develop a new method or procedure for the fast and accurate offsite consequence calculation for the multi-unit NPP accident analysis. Since the multi-unit level 3 PSA is being currently performed assuming that all the NPPs are located at the same position such as a center of mass (COM) or base NPP position, radionuclide concentrations or exposure doses near NPPs can be drastically distorted depending on the locations, multi-unit NPP alignment, and the wind direction. In order to overcome this disadvantage of the COM method, the idea of a new multiple location (ML) method was proposed and implemented into a new tool MURCC (multi-unit radiological consequence calculator). Furthermore, the MURCC code was further improved for the multi-unit level 3 PSA that has the arbitrary number of multi-unit NPPs. The objectives of this study are to (1) qualitatively and quantitatively compare COM and ML methods, and (2) demonstrate the strength and efficiency of the ML method. The strength of the ML method was demonstrated by the applications to the multi-unit long-term station blackout (LTSBO) accidents at the four-unit Vogtle NPPs. Thus, it is strongly recommended that this ML method be employed for the offsite consequence analysis of the multi-unit NPP accidents.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2018년도 춘계 학술논문 발표대회
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• pp.151-152
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• 2018

In the domestic nuclear power industry, concern about safety of nuclear power plants is continuously increased with the Fukushima nuclear power plant accident. In order to enhance the safety of nuclear power plants, it is important to ensure that the power plants are operating with proper margin within the original design bases. Margin management is the process of ensuring that the NPP designer and operator are aware of the physical and operating limits, and potential and probability of failure, for each component in the plant. All components are subject to margin considerations, but the most important components by scope and attention are those related to safety-related systems and NPP safe shutdown.

• Nuclear Engineering and Technology
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• 제49권7호
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• pp.1431-1441
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• 2017

The Fukushima accident reveals the vulnerability of existing active nuclear power plant (NPP) design against prolonged loss of external electricity events. The passive safety system is considered an attractive alternative to cope with this kind of disaster. Also, the passive safety system enhances both the safety and the economics of NPPs. The adoption of a passive safety system reduces the number of active components and can minimize the construction cost of NPPs. In this paper, reflecting on the experience during the development of the APR+ design in Korea, we propose the concept of an innovative Power Reactor (iPower), which is a kind of passive NPP, to enhance safety in a revolutionary manner. The ultimate goal of iPower is to confirm the feasibility of practically eliminating radioactive material release to the environment in all accident conditions. The representative safety grade passive system includes a passive emergency core cooling system, a passive containment cooling system, and a passive auxiliary feedwater system. Preliminary analysis results show that these concepts are feasible with respect to preventing and/or mitigating the consequences of design base accidents and severe accidents.

• 시스템엔지니어링학술지
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• 제18권2호
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.