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[en] The minimum DNBR (MDNBR) for prevention of the boiling crisis and the fuel clad melting is very important factor that should be consistently monitored in safety aspects. Artificial intelligence methods have been extensively and successfully applied to nonlinear function approximation such as the problem in question for predicting DNBR values. In this paper, support vector regression (SVR) model and fuzzy neural network (FNN) model are developed to predict the MDNBR using a number of measured signals from the reactor coolant system. Also, two models are trained using a training data set and verified against test data set, which does not include training data. The proposed MDNBR estimation algorithms were verified by using nuclear and thermal data acquired from many numerical simulations of the Yonggwang Nuclear Power Plant Unit 3 (YGN-3)
[en] We have investigated the catalytic activity of binuclear Ru-complexes exhibiting comparable or higher activity to those of the corresponding mononuclear Ru-complexes in the ring-closing metathesis reaction. We also observed for the first time that the catalytic activity of both monomeric and dimeric Hoveyda-Grubbs first-generation Ru-complexes showed inverse temperature dependency. Further studies on elucidation the origin of this unusual temperature dependency are underway. Olefin metathesis has become one of the simplest and most effective synthetic methods for carbon-carbon double bond construction. Much of the recent successful progress in ring-closing metathesis stems largely from the availability of several well defined mononuclear Ru-complexes such as. On the other hand, numbers of binuclear and trinuclear molybdenum-based Schrock-type and ruthenium-based Grubbs-type complexes have also been developed, particularly, for the preparation of di- or tri-block, star-shape block copolymers. Despite their potential for RCMs, to our best knowledge, no investigation has been made for RCMs with binuclear Ru-complexes
[en] In this study, a lock-in technique and power adjustment were applied to the cooling device for the IR thermography in order to detect the wall-thinned defects of the pipe specimen in a normal operation NPPs. According as the number of the cooling devices is increased and air volume transferred by the cooling device increases, wall-thinned defects inside pipes are more visible. By cooling the pipe using a lock-in technique in IR thermography, the boundary of the wall-thinned defective part is clear and the defect detection is easy. It is expected to detect the wall-thinned defects of piping during normal operation, to shorten the maintenance time of the NPPs, and to improve the work efficiency of the inspector. Recently, the safety problem of nuclear power plants (NPPs) has emerged as a global concern. As a result, the secondary system equipment in long-term aged NPPs has been growing interest. For these reasons, NDT for checking the integrity of the secondary system equipment is performed. The infrared (IR) thermography is one of the NDT. It is possible for us to solve the problems of the existing NDT. IR thermography can detect without contact the wall-thinned defects in pipes. Also, IR thermography using a lock-in technique for inspection is much safer and faster than other techniques. It is expected to be able to accurately detect the boundary of the non-defect parts and the defect parts, and shows a high utilization in the industrial field. Through this study, we have developed the inspection technique that can detect the defects by using the lock-in technique in IR thermography for inspection of pipes in the NPPs during the normal operation
[en] Recently, severe accidents of the nuclear power plants (NPPs) have become globally an impending concern. In this paper, severe accidents were analyzed based on OPR1000. The increase of the hydrogen concentration in severe accidents is one of the factors threatening the integrity of the containment. It was determined that a method using fuzzy neural network (FNN) has been developed for predicting the hydrogen concentration. And the FNN model was verified based on the NPPs simulation data acquired by using MAAP4 code. It is expected that the containment can be kept safely because the hydrogen concentration can be predicted well at the beginning of the real accident
[en] Wall-thinned defects caused by accelerated corrosion due to fluid flow in the inner pipe appear in many structures of the secondary systems in nuclear power plants (NPPs) and are a major factor in degrading the integrity of pipes. Wall-thinned defects need to be managed not only when the NPP is under maintenance but also when the NPP is in normal operation. To this end, a test technique was developed in this study to detect such wall-thinned defects based on the temperature difference on the surface of a hot pipe using infrared (IR) thermography and a cooling device. Finite element analysis (FEA) was conducted to examine the tendency and experimental conditions for the cooling experiment. Based on the FEA results, the equipment was configured before the cooling experiment was conducted. The IR camera was then used to detect defects in the inner pipe of the pipe specimen that had artificially induced defects. The IR thermography developed in this study is expected to help resolve the issues related to the limitations of non-destructive inspection techniques that are currently conducted for NPP secondary systems and is expected to be very useful on the NPPs site.
[en] Recently, severe accidents in nuclear power plants (NPPs) have become a global concern. The aim of this paper is to predict the hydrogen buildup within containment resulting from severe accidents. The prediction was based on NPPs of an optimized power reactor 1,000. The increase in the hydrogen concentration in severe accidents is one of the major factors that threaten the integrity of the containment. A method using a fuzzy neural network (FNN) was applied to predict the hydrogen concentration in the containment. The FNN model was developed and verified based on simulation data acquired by simulating MAAP4 code for optimized power reactor 1,000. The FNN model is expected to assist operators to prevent a hydrogen explosion in severe accident situations and manage the accident properly because they are able to predict the changes in the trend of hydrogen concentration at the beginning of real accidents by using the developed FNN model.
[en] It is very difficult for nuclear power plant operators to monitor and identify the major severe accident scenarios following an initiating event by staring at temporal trends of important parameters. The objective of this study is to develop and verify the monitoring for severe accidents using 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). The SVC and PNN are used for event classification among the severe accidents. Also, GMDH and FNN are used to monitor for severe accidents. The inputs to AI techniques are initial time-integrated values obtained by integrating measurement signals during a short time interval after reactor scram. In this study, 3 types of initiating events such as the hot-leg LOCA, the cold-leg LOCA and SGTR are considered and it is verified how well the proposed scenario identification algorithm using the GMDH and FNN models identifies the timings when the reactor core will be uncovered, when CET will exceed 1200 .deg. F and when the reactor vessel will fail. In cases that an initiating event develops into a severe accident, the proposed algorithm showed accurate classification of initiating events. Also, it well predicted timings for important occurrences during severe accident progression scenarios, which is very helpful for operators to perform severe accident management
[en] Recently, various inspection techniques for improving the safety of nuclear power plants (NPPs) are being studied. Wall-thinned defect of the pipe are a major cause of reducing the NPP integrity. The purpose of this study was to detect the wall-thinned defects of Nuclear Power Plant (NPP) pipes using the lock-in infrared (IR) thermography method. When using the technique of lock-in IR thermography to detect wall-thinned defects of the pipe, it is very important to select the appropriate lock-in frequency. In this study, we applied a cooling and heating method for detecting wall-thinned defects of the pipe of NPPs
[en] Until now, nuclear power has been only used for the base load power operation. However, current nuclear power plants are recognized as the most reasonable energy source. As a result, the proportion of nuclear power has being grown increasingly. Therefore, load following operation of a nuclear power plant should be an essential option. Most of the existing nuclear power plants perform reactor operation by varying the boron concentration in the coolant. But it is hard to respond quickly to demands for the power changes. In case of using the control rods, reactivity control is easy, but axial power distribution control is very hard because it has very complex and nonlinear dynamic characteristics. In this study, we have introduced a Model Predictive Control (MPC) method to control the average coolant temperature and Axial Shape Index (ASI) automatically at the same time, and we have improved the performance of controller by applying the Genetic Algorithm (GA) to optimize the control rod movement
[en] Recently, the number of the life-extended nuclear power plants (NPPs) is increasing. Thus, the degradation can occur in the various structures of the NPP secondary systems caused by the fatigue or corrosion, etc. Among these problems, the wall-thinned defect by the fluids of the inner wall can break the pipe due to the local stress concentrations. This cases have already emerged as an important issue in terms of ensuring the soundness and safety in NPPs. There are many NDT techniques to detect the wall-thinned defect from the inner wall. The infrared thermography which is one of these techniques provides real-time images by scanning the temperature of the target surface and then, converting it to the temperature. This technique can solve the existing problems by identifying the presence or absence of the defect through observation of the temperature difference