• Journal of IKEEE
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• v.27 no.1
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• pp.103-108
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• 2023

This research was carried out experiments with changing processes and design parameters to optimally design a SiC-based 1200V power MOSFET, and then, essential electrical characteristics were derived. In order to secure the excellence of the trench gate type SiC power MOSFET device to be designed, electrical characteristics were derived by designing it under conditions such as planner gate SiC power MOSFET, and it was compared with the trench gate type SiC power MOSFET device. As a result of the comparative analysis, the on-resistance while maintaining the yield voltage was 1,840mΩ, for planner gate power MOSFET and to 40mΩ for trench gate power MOSFET, respectively, indicating characteristics more than 40 times better. It was judged that excellent results were derived because the temperature resistance directly affects energy efficiency. It is predicted that the devices optimized through this experiment can sufficiently replace the IGBT devices generally used in 1200V class, and that since the SiC devices are wide band gap devices, they will be widely used to apply semiconductors for vehicles using devices with excellent thermal characteristics.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.133-140
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• 2023

The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.

• Nuclear Engineering and Technology
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• v.23 no.1
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• pp.66-80
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• 1991

The LOFT intermediate break experiment L5-1, which simulates 12 inch diameter ECC line break in a typical PWR, has been analyzed using the reactor thermal/hydraulic analysis code RELAP5/MOD2, Cycle 36.04. The base calculation, which modeled the core with single flow channel and two heat structures without using the options of reflood and gap conductance model, has been successfully completed and compared with experimental data. Sensitivity studies were carried out to investigate the effects of nodalization at reactor vessel and core modeling on major thermal hydraulic parameters, especially on peak cladding temperature(PCT). These sensitivity items are : single flow channel and single heat structure (Case A), two flow channel and two heat structures (Case B), reflood option added (Case C) and both reflood and gap conductance options added (Case D). The code, RELAP5/MOD2 Cycle 36.04 with the base modeling, predicted the key parameters of LOFT IBLOCA Test L5-1 better than Cases A,B,C and D. Thus, it is concluded that the single flow channel modeling for core is better than the two flow channel modeling and two heat structure is also better than single heat structure modeling to predict PCT at the central fuel rods. It is, therefore, recommended to use the reflood option and not to use gap conductance option for this L5-1 type IBLOCA.

• Korean Journal of Environmental Biology
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• v.40 no.3
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• pp.341-351
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• 2022

Among various environmental stresses, humid stress lacks mechanisms and biochemical understanding compared to drought, low temperature, and high salt stresses. The aim of this study was to investigate enzyme activity of field crops under humidity stress. Results of this study could be used as basic data for understanding humidity stress and early diagnosis. Growth and enzyme activities of sesame, perilla, red beans, sorghum, and beans as major field crops in Korea when flooded were investigated. It was confirmed that growths of both shoots and roots were retarded. In plants, anaerobic fermentation occurred due to flooding stress, which increased the activity of alcohol dehydrogenase (ADH) compared to the control group. Increases of reactive oxygen species (ROS) were also observed. All flooded plants showed increased peroxidase (POD) activity and lipid peroxidation. Their dyeing strength was darker than that of the control group, even in 3,3'-diaminobenzidine (DAB) staining. Since enzyme activity changes in plants appear relatively faster than changes in phenotype at the ground level, they could be used as biomarkers for early diagnosis of humidity stress in crops.

• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.1
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• pp.1-9
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• 2022

This study was carried out to compare the DMY (dry matter yield) of IRG (Italian ryegrass) in the southern coastal regions of Korea due to seasonal climate scenarios such as the Kaul-Changma (late monsoon) in autumn, extreme winter cold, and drought in the next spring. The IRG data (n = 203) were collected from various Reports for Collaborative Research Program to Develop New Cultivars of Summer Crops in Jeju, 203 Namwon, and Yeungam from the Rural Development Administration - (en DASH). In order to define the seasonal climate scenarios, climate variables including temperature, humidity, wind, sunshine were used by collected from the Korean Meteorological Administration. The discriminant analysis based on 5% significance level was performed to distinguish normal and abnormal climate scenarios. Furthermore, the DMY comparison was simulated based on the information of sample distribution of IRG. As a result, in the southern coastal regions, only the impact of next spring drought on DMY of IRG was critical. Although the severe winter cold was clearly classified from the normal, there was no difference in DMY. Thus, the DMY comparison was simulated only for the next spring drought. Under the yield comparison simulation, DMY (kg/ha) in the normal and drought was 14,743.83 and 12,707.97 respectively. It implies that the expected damage caused by the spring drought was about 2,000 kg/ha. Furthermore, the predicted DMY of spring drought was wider and slower than that of normal, indicating on high variability. This study is meaningful in confirming the predictive DMY damage and its possibility by spring drought for IRG via statistical simulation considering seasonal climate scenarios.

• Journal of Korea Water Resources Association
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• v.55 no.4
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• pp.301-308
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• 2022

Algae is an indispensable primary producer in the ecosystem by supplying energy to consumers in the aquatic ecosystem, and is largely divided into green algae, blue-green algae, and diatoms. In the case of blue-green algae, the water temperature rises, which occurs in the summer and overgrows, which is the main cause of the algae bloom. Recently, the change in the occurrence time and frequency of the algae bloom is increasing due to climate change. Existing algae survey methods are performed by collecting water and measuring through sensors, and time, cost and manpower are limited. In order to overcome the limitations of these existing monitoring methods, research has been conducted to perform remote monitoring using spectroscopic devices such as multispectral and hyperspectral using satellite image, UAV, etc. In this study, we tried to confirm the possibility of species classification of remote monitoring through laboratory-scale experiments through algal culture and river water collection. In order to acquire hyperspectral images, a hyperspectral sensor capable of analyzing at 400-1000 nm was used. In order to extract the spectral characteristics of the collected river water for classification of algae species, filtration was performed using a GF/C filter to prepare a sample and images were collected. Radiation correction and base removal of the collected images were performed, and spectral information for each sample was extracted and analyzed through the process of extracting spectral information of algae to identify and compare and analyze the spectral characteristics of algae, and remote sensing based on hyperspectral images in rivers and lakes. We tried to review the applicability of monitoring.

• Tunnel and Underground Space
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• v.33 no.3
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• pp.189-207
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• 2023

In the present study, the thermoshearing experiment on a rough rock fracture were modeled using a three-dimensional grain-based distinct element model (GBDEM). The experiment was conducted by the Korea Institute of Construction Technology to investigate the progressive shear failure of fracture under the influence of thermal stress in a critical stress state. The numerical model employs an assembly of multiple polyhedral grains and their interfaces to represent the rock sample, and calculates the coupled thermo-mechanical behavior of the grains (blocks) and the interfaces (contacts) using 3DEC, a DEM code. The primary focus was on simulating the temperature evolution, generation of thermal stress, and shear and normal displacements of the fracture. Two fracture models, namely the mated fracture model and the unmated fracture model, were constructed based on the degree of surface matedness, and their respective behaviors were compared and analyzed. By leveraging the advantage of the DEM, the contact area between the fracture surfaces was continuously monitored during the simulation, enabling an examination of its influence on shear behavior. The numerical results demonstrated distinct differences depending on the degree of the surface matedness at the initial stage. In the mated fracture model, where the surfaces were in almost full contact, the characteristic stages of peak stress and residual stress commonly observed in shear behavior of natural rock joints were reasonably replicated, despite exhibiting discrepancies with the experimental results. The analysis of contact area variation over time confirmed that our numerical model effectively simulated the abrupt normal dilation and shear slip, stress softening phenomenon, and transition to the residual state that occur during the peak stress stage. The unmated fracture model, which closely resembled the experimental specimen, showed qualitative agreement with the experimental observations, including heat transfer characteristics, the progressive shear failure process induced by heating, and the increase in thermal stress. However, there were some mismatches between the numerical and experimental results regarding the onset of fracture slip and the magnitudes of fracture stress and displacement. This research was conducted as part of DECOVALEX-2023 Task G, and we expect the numerical model to be enhanced through continued collaboration with other research teams and validated in further studies.

• Journal of the Korean Institute of Gas
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• v.27 no.2
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• pp.71-78
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• 2023

In general, electric submersible pumps (ESPs), which have an average life of 1.0 to 1.5 years, experience a decrease in performance and a reduction in life of the pump depending on oil and gas reservoir characteristics and operating conditions in wells. As the result, the failure of ESP causes high well workover costs due to retrieval and installation, and additional costs due to shut down. In this study, a flow loop system was designed and established to predict the life of ESP in long­term operation of oil and gas wells, and the life cycle data of ESP from the time of installation to the time of failure was acquired and analyzed. Among the data acquired from the system, flow rate, inlet and outlet temperature and pressure, and the data of the vibrator installed on the outside of ESP were analyzed, and then the performance status according to long-term operation was classified into five stages: normal, advice I, advice II, maintenance, and failed. Through the experiments, it was found that there was a difference in the data trend by stage during the long­term operation of the ESP, and then the condition of the ESP was diagnosed and the failure of the pump was predicted according to the operating time. The results derived from this study can be used to develop a failure prediction program and data analysis algorithm for monitoring the condition of ESPs operated in oil and gas wells.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.43-45
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• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.

• Composites Research
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• v.36 no.3
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• pp.211-216
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• 2023

When designing ships and aircraft structures, it is important to design them to satisfy weight reduction and strength. Currently, studies related to topology optimization using 3D printed composite materials are being actively conducted to satisfy the weight reduction and strength of the structure. In this study, structural analysis was performed to analyze the applicability of 3D printed composite materials to the flight control surface, one of the parts of an aircraft or unmanned aerial vehicle. The optimal topology shape of the flight control surface for the bending load was analyzed by considering three types (hexagonal, rectangular, triangular) of the topology shape of the flight control surface. In addition, the bending strength of the flight control surface was analyzed when four types of reinforcing materials (carbon fiber, glass fiber, high-strength high-temperature glass fiber, and kevlar) of the 3D printed composite material were applied. As a result of comparing the three-point bending test results with the finite element method results, it was confirmed that the flight control surface with hexagonal topology shape made of carbon fiber and Kevlar had excellent performance. And it is judged that the 3D printed composite can be sufficiently applied to the flight control surface.