• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2547-2555
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• 2021

The frequency of reactor coolant leakage is expected to increase over the lifetime of a nuclear power plant owing to degradation mechanisms, such as flow-acceleration corrosion and stress corrosion cracking. When loss of coolant accidents (LOCAs) occur, several parameters change rapidly depending on the size and location of the cracks. In this study, leak flow during LOCAs is predicted using a deep fuzzy neural network (DFNN) model. The DFNN model is based on fuzzy neural network (FNN) modules and has a structure where the FNN modules are sequentially connected. Because the DFNN model is based on the FNN modules, the performance factors are the number of FNN modules and the parameters of the FNN module. These parameters are determined by a least-squares method combined with a genetic algorithm; the number of FNN modules is determined automatically by cross checking a fitness function using the verification dataset output to prevent an overfitting problem. To acquire the data of LOCAs, an optimized power reactor-1000 was simulated using a modular accident analysis program code. The predicted results of the DFNN model are found to be superior to those predicted in previous works. The leak flow prediction results obtained in this study will be useful to check the core integrity in nuclear power plant during LOCAs. This information is also expected to reduce the workload of the operators.

• Tribology and Lubricants
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• v.34 no.2
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• pp.43-48
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• 2018

This paper presents the thermohydrodynamic analysis of tilting journal pad bearings supporting a power turbine rotor applied to a 250 kW super-critical $CO_2$ cycle. In the analysis, the generalized Reynolds equation and 3D energy equation are solved to predict oil film temperature and the 3D heat conduction equation is solved for pad temperature. The power turbine rotor is supported by two tilting pad bearings consisting of five pads with an oil supply block between the pads. Copper backing pads with higher thermal conductivity compared to steel backing pads are adopted to improve thermal management. The predicted maximum pad temperature is around $55^{\circ}C$ which is approximately $15^{\circ}C$ higher than oil supply temperature. In addition, the predicted minimum film thickness is 50 mm at a rotating speed of 5,000 rpm. These results indicate that there is no issue in the thermal behavior of the bearing. An operation test is performed with a power turbine module consisting of a power turbine, a reduction gear and a generator. Thermocouples are installed at the 75% position from the leading edge of the pad to monitor pad temperature. The power turbine uses compressed air at a temperature of $250^{\circ}C$ in its operation. The steady state pad temperatures measured in the test show good agreement with the predicted temperatures.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.21 no.5
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• pp.391-404
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• 2009

A small hydropower plant(SHP) using cooling water discharged from the power plant was constructed in Samcheonpo. This study presents predicted and measured hydrological data in the construction process of small hydropower plant in order to evaluate characteristics of water level variation of cooling water discharge channel which is a key factor in the design of SHP since the water level rise of channel is related to impact on circulating water system of the existing power plant. Various methods were applied for prediction of water level variation in the design stage from simple empirical formula to sophisticated 3-dimensional CFD method. Measured results reveal that mean value was similar between measured and predicted, but measured results were larger than predicted in deviation. Moreover, simple formula, i.e. standard weir equation and Honma equation, were more useful before installation of SHP, but sophisticated methods during operation of SHP.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.1-6
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• 2009

The traditional demand prediction was based on the technique wherein electric power corporations made monthly or seasonal estimation of electric power consumption for each area and subscription type for the next one or two years to consider both seasonally generated and local consumed amounts. Note, however, that techniques such as pricing, power generation plan, or sales strategy establishment were used by corporations without considering the production, comparison, and analysis techniques of the predicted consumption to enable efficient power consumption on the actual demand side. In this paper, to calculate the predicted value of electric power consumption on a short-term basis (15 minutes) according to the amount of electric power actually consumed for 15 minutes on the demand side, we performed comparison and analysis by applying a 15-minute interval prediction technique to the average and that to the time series analysis to show how they were made and what we obtained from the simulations.

• Proceedings of the KIPE Conference
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• 2006.06a
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• pp.488-491
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• 2006

In this paper proposed method of maximum power point tracking using boost converter for a connected single phase inverter with photovoltaic system. The maximum power point tracking control is based on generated circuit control MOSFET switch of boost converter and single phase inverter uses predicted current control to control four IGBT's switch in full bridge. The predicted current control provide current with sinusoidal wave shape and inphase with voltage. The generation control circuit allows each photovoltaic module to operate independently at peak capacity, simply by detecting of the output power of the system. Furthermore, the generation control circuit attenuates low-frequency ripple voltage, which is caused by the full-bridge inverter, across the photovoltaic modules. Consequently, the output power of system is increased due to the increase in average power generated by the photovoltaic modules. The effectiveness of the proposed inverter system is confirmed experimentally and by means of simulation.

• Journal of Environmental Impact Assessment
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• v.9 no.1
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• pp.61-74
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• 2000

This study compares the results of environmental impact assessment with the results of post-environmental investigation, using the case of Taean thermal power plant construction. The atmospheric and water qualities were not greatly changed before and after the construction of the power plant. However, the site of the highest concentration predicted by the atmospheric quality modeling in environmental impact assessment was different from that after operation of 4 power plants. There was also a difference in the diffusion range of thermal discharge water between the measured result(1km) and the predicted value(1.5km) with the model. Thus, environmental impact evaluation should be based on long-term (more than a year) environmental monitoring data. For the modeling of atmospheric quality and numerical thermal discharge water diffusion, appropriate models for each plant should be selected and the numerical modeling should be accompanied by computer simulation, wind tunnel test, etc. Moreover, environmental evaluation should focus more on the degree of impact on surroundings than the prediction of changes in surroundings caused by operation of plants.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.860-865
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• 2021

A data-based model, such as an AAKR model is widely used for monitoring the drifts of sensors in nuclear power plants. However, since a training dataset and a test dataset for a data-based model cannot be constructed with the data from all the possible states, the model uncertainty cannot be good enough to represent the uncertainty of estimations. In fact, the errors of estimation grow much bigger if the incoming data come from inexperienced states. To overcome this limitation of the model uncertainty, a new measure of uncertainty for a data-based model is developed and the predicted uncertainty is introduced. The predicted uncertainty is defined in every estimation according to the incoming data. In this paper, the AAKR model is used as a data-based model. The predicted uncertainty is similar in magnitude to the model uncertainty when the estimation is made for the incoming data from the experienced states but it goes bigger otherwise. The characteristics of the predicted model uncertainty are studied and the usefulness is demonstrated with the pressure signals measured in the flow-loop system. It is expected that the predicted uncertainty can quite reduce the false alarm by using the variable threshold instead of the fixed threshold.

• IE interfaces
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• v.21 no.1
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• pp.85-95
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• 2008

Reliability prediction serves many purposes during the life of a system, so several methods have been developed to predict the parts and systems reliability. MIL-HDBK-217F, among the those methods, has been widely used as a requisite tool for the reliability prediction which is applied to nuclear power plants and their safety regulations. This paper presents the reliability prediction for the DSP(Digital Signal Processor) module composed of three assemblies. One of the assemblies has a monitoring and self test function which is used to enhance the module reliability. The reliability of each assembly is predicted by MIL-HDBK-217F. Based on these predicted values, Markov modelling is finally used to predict the module reliability. Relax 7.7 software of Relax software corporation is used because it has many part libraries and easily handles Markov processes modelling.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1338-1343
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• 2007

High-efficient machining, which means cutting a part in the least amount of time, is the most effective tool to improve productivity. In this study, a new feed optimization method based on the cutting power regulation was proposed to realize the high-efficient machining in turning process. The cutting area was evaluated by using the Boolean intersection operation between the cutting tool and workpiece. And the cutting force and power were predicted from the cutting parameters such as feed, depth of cut, spindle speed, specific cutting force, and so on. Especially, the reliability of the proposed optimization method was validated by comparing the predicted and measured cutting forces. The simulation results showed that the proposed optimization method could effectively enhance the productivity in turning process.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1995.11a
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• pp.85-88
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• 1995

In this report the large-signal RF performance of GaN MESFETs at different operating temperatures is investigated using a harmonic balance modeling technique. The predicted device performance calculated by the large-si anal model of a GaN FET is shown to be in good agreement with experimen tar data. It is demonstrated that the optimal RF performance of a GaN MESFET amplifier is achieved by balancing the input impedence for a optimized de sign. A GaN MESFET with the optimized design is predicted to produce maximum RF output power of about 4W/mm and 1W/mm at room temperature and 773 K, respectively. The device produces a peak Power-Added Efficiency (PAE) of 52% and 32% at the two temperatures.