• Journal of the Korean Solar Energy Society
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• v.31 no.4
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• pp.128-135
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• 2011

일반적으로 PEM 연료전지에서는 수분 균형이 시스템의 효율에 결정적으로 영향을 미치기 때문에, 이에 대한 균형(balance)을 잡는 것이 매우 중요하다. 특히, 촉매 층에서 물이 넘치는 익수현상(flooding)이나 건조현상(drying)이 발생하게 되면 연료전지의 효율이 급격하게 저하하므로, 항상 수분의 균형이 잡히도록 시스템을 제어하는 것이 일반적이다. 이 때,수분의 익수현상이나 건조현상은 PEM 연료전지의 용량과 주위의 환경, 즉 온도와 습도에 많은 영향을 받게 된다. 금번 논문에서는 가정용 규모인 3kW급에서 10kW급까지의 PEM 연료전지를 설치하였을 때, 주위의 환경(온도와 습도)이 수분 이동에 어떠한 영향을 미치는 지를 시간에 따라서 시뮬레이션(simulation)한 결과를 보여주고 있다. 결과에서 유입공기의 온도가 $50^{\circ}C$ 이하일 경우, 고정부하가 5kW급 이하이면 대부분이 건조현상이 발생하였으나, 고정부하가 6kW급 이상이 되면 익수현상이 운전시간이 20분 이내에서 발생하였다. 또한 고정부하를 최고 10kW급까지 올린 경우, 유입공기의 온도가 $50^{\circ}C$까지는 익수현상이 발생하였으나 $60^{\circ}C$ 이상인 경우에는 거의 건조현상이 발생함을 알 수 있었다.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• Journal of Power Electronics
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• v.19 no.3
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• pp.702-713
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• 2019

Multilevel Inverters (MLIs) are widely used in medium voltage applications due to their various advantages. In addition, there are numerous types of MLIs for such applications. However, the diode-less 3-level (3L) T-type Neutral Point Clamped (NPC) MLI is the most advantageous due to its low conduction losses and high potential efficiency. The power circuit of a 3L T-type NPC is derived by the conventional two level inverter by a slight modification. In order to explore the MLI performance for various Pulse Width Modulation (PWM) schemes, this paper examines the operation of a 3L (five level line to line) T-type NPC MLI for various types of Multi-Carriers Pulse Width Modulation (MCPWM) schemes. These PWM schemes are compared in terms of their voltage profile, total harmonic distortion (THD) and conduction losses. In addition, a 3L T-type NPC MLI is also compared with the conventional NPC in terms of number of switches, clamping diodes, main diodes and capacitors. Moreover, the capacitor-balancing problem is also investigated using the Neutral Point Fluctuation (NPF) method with all of the MCPWM schemes. A 1kW 3L T-type NPC MLI is simulated in MATLAB/Simulink and implemented experimentally and its performance is tested with a 1HP induction motor. The results indicate that the 3L T-type NPC MLI has better performance than conventional NPC MLIs.

• Nutrition Research and Practice
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• v.15 no.1
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• pp.95-105
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• 2021

BACKGROUND/OBJECTIVES: The measurement of body composition, including muscle and fat mass, remains challenging in large epidemiological studies due to time constraint and cost when using accurate modalities. Therefore, this study aimed to develop and validate prediction equations according to sex to measure lean body mass (LBM), appendicular skeletal muscle mass (ASM), and body fat mass (BFM) using anthropometric measurement, serum creatinine level, and lifestyle factors as independent variables and dual-energy X-ray absorptiometry as the reference method. SUBJECTS/METHODS: A sample of the Korean general adult population (men: 7,599; women: 10,009) from the Korean National Health and Nutrition Examination Survey 2008-2011 was included in this study. The participants were divided into the derivation and validation groups via a random number generator (with a ratio of 70:30). The prediction equations were developed using a series of multivariable linear regressions and validated using the Bland-Altman plot and intraclass correlation coefficient (ICC). RESULTS: The initial and practical equations that included age, height, weight, and waist circumference had a different predictive ability for LBM (men: R2 = 0.85, standard error of estimate [SEE] = 2.7 kg; women: R2 = 0.78, SEE = 2.2 kg), ASM (men: R2 = 0.81, SEE = 1.6 kg; women: R2 = 0.71, SEE = 1.2 kg), and BFM (men: R2 = 0.74, SEE = 2.7 kg; women: R2 = 0.83, SEE = 2.2 kg) according to sex. Compared with the first prediction equation, the addition of other factors, including serum creatinine level, physical activity, smoking status, and alcohol use, resulted in an R2 that is higher by 0.01 and SEE that is lower by 0.1. CONCLUSIONS: All equations had low bias, moderate agreement based on the Bland-Altman plot, and high ICC, and this result showed that these equations can be further applied to other epidemiologic studies.

• Journal of Appropriate Technology
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• v.7 no.2
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• pp.225-234
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• 2021

The purpose of this paper is to examine commercialization model of appropriate technology through the case of the Lao-Korea Science and Technology Center (LKSTC). LKSTC has developed washing, water treatment, and sterilization technology in the agrifood sector and three types of pico-hydro generator, Pico-solar hybrid system, and energy remote monitoring technology in the renewable energy sector. Commercialization of appropriate technology was successfully carried out through the establishment of Kaipan community business, school enterprises, and social enterprise. The policy implications are as follows. First, the commercialization of appropriate technology in developing countries should enhance the linkage with the regional development policies of the recipient countries. Second, in order to minimize market risk, innovative technology development and local startup networks should be properly established. Finally, the mid and long term efforts are needed to increase the sustainability of the business.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.1970-1978
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• 2021

The aim of this study was to optimize the target, moderator, and collimator (TMC) in a neutron beam generator for the accelerator-based BNCT (A-BNCT) system. The optimization employed the Monte Carlo Neutron and Photon (MCNP) simulation. The optimal geometry for the target was decided as the one with the highest neutron flux among nominates, which were called as angled, rib, and tube in this study. The moderator was optimized in terms of consisting material to produce appropriate neutron energy distribution for the treatment. The optimization of the collimator, which wrapped around the target, was carried out by deciding the material to effectively prevent the leakage radiations. As results, characteristic of the neutron beam from the optimized TMC was compared to the recommendation by the International Atomic Energy Agent (IAEA). The tube type target showed the highest neutron flux among nominates. The optimal material for the moderator and collimator were combination of Fluental (Al₂0₃+AlF₃) with ⁶⁰Ni filter and lead, respectively. The optimized TMC satisfied the IAEA recommendations such as the minimum production rate of epithermal neutrons from thermal neutrons: that was 2.5 times higher. The results can be used as source terms for shielding designs of treatment rooms.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.545-550
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• 2023

A pretensioner is a safety device that protects occupants by pulling the seat belt in the event of a vehicle collision. However, since the pretensioner is driven by a explosive method, it is necessary to replace not only the gas generator but also all connecting parts including the manifold after an accident. Therefore, in this paper, we propose an elastic force-based pretensioner that can be used safely and semi-permanently. After analyzing the operating mechanism of the existing pretensioner from a thermodynamic/dynamic point of view, the spring stiffness that can be deployed within an appropriate operating time was determined by converting the gas explosion energy into elastic energy. In addition, the coil spring shape that satisfies the elastic stiffness was designed in consideration of the vehicle interior installation standard. Finally, the operating performance of the pretensioner driven by elastic force was verified through fabrication.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.18 no.4
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• pp.12-21
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• 2022

Steam assisted gravity drainage(SAGD) is a process that drills well in the underground oil sands layer, injects hightemperature steam, lowers the viscosity of buried bitumen, and recovers it to the ground. Recently, direct contact steam generator(DCSG) is being developed to maximize steam efficiency for SAGD process. The DCSG requires high technology to achieve pressurized combustion and steam generation in accordance with underground pressurized conditions. Therefore, it is necessary to develop a combustion technology that can control the heat load and exhaust gas composition. In this study, process analysis of high-pressurized DCSG was conducted to apply oxygen enrichment technology in which nitrogen of the air was partially removed for increasing steam production and reducing fuel consumption. As the process analysis conditions, methane as the fuel and normal air or oxygen enriched air as the oxidizing agent were applied to high-pressurized DCSG process model. A simple combustion reaction program was used to calculate the property variations for combustion temperature, steam ratio and residual heat in exhaust gas. As a major results, the steam production efficiency of DCSG using the pure oxygen was about 6% higher than that of the normal air due to the reducing nitrogen in the air. The results of this study will be used as operating data to test the demonstration device.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.77-87
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• 2023

Wind turbines playing a critical role in renewable energy generation, accurately assessing their operational status is crucial for maximizing energy production and minimizing downtime. This study conducts a comparative analysis of different neural network models for wind turbine condition diagnosis, evaluating their effectiveness using a dataset containing sensor measurements and historical turbine data. The study utilized supervisory control and data acquisition data, collected from 2 MW doubly-fed induction generator-based wind turbine system (Model HQ2000), for the analysis. Various neural network models such as artificial neural network, long short-term memory, and recurrent neural network were built, considering factors like activation function and hidden layers. Symmetric mean absolute percentage error were used to evaluate the performance of the models. Based on the evaluation, conclusions were drawn regarding the relative effectiveness of the neural network models for wind turbine condition diagnosis. The research results guide model selection for wind turbine condition diagnosis, contributing to improved reliability and efficiency through advanced neural network-based techniques and identifying future research directions for further advancements.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3464-3466
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• 2007

Power uprate is the process of increasing the maximum power level at which a commercial nuclear power plant may operate. Power uprate applications(113 units) for NPPs(Nuclear Power Plants) were recently approved in the United States. Utilities have been using power uprates since the 1970s as a way of increasing the power output of their nuclear plants. To increase the power output of a reactor, typically more highly enriched uranium fuel and/or more fresh fuel is used. This enables the reactor to produce more thermal energy and therefore more steam, driving a turbine generator to produce electricity. In this paper, the propriety of power uprate is explained through the review on the power uprate method and the changes of the physical parameters due to power uprate. The analysis results showed that the CDF(Core Damage Frequency) and LERF(Large Early Release Frequency) are affected in the current probabilistic safety assessment (PSA) model.