• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.3
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• pp.180-188
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• 2014

Hyundai Motor Group(HMG) carried out experimental investigation of sunroof buffeting phenomena on a simplified car model called Hyundai simplified model(HSM). HMG invited participation from commercial CFD vendors to perform numerical investigation of sunroof buffeting for HSM model with a goal to determine whether CFD can predict sunroof buffeting behavior to sufficient accuracy. ANSYS Korea participated in this investigation and performed numerical simulations of sunroof buffeting for HSM using ANSYS fluent, the general purpose CFD code. First, a flow field validation is performed using closed sunroof HSM model for 60 km/h wind speed. The velocity profiles at three locations on the top surface of HSM model are predicted and compared with experimental measurement. Then, numerical simulations for buffeting are performed over range of wind speeds, using advanced scale resolving turbulence model in the form of detached eddy simulation (DES). Buffeting frequency and buffeting level are predicted in simulation and compared with experimental measurement. With reference to comparison between experimental measurements with CFD predictions of buffeting frequency and level, conclusion are drawn about predictive capabilities of CFD for real vehicle development.

• Journal of Korea Water Resources Association
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• v.46 no.9
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• pp.933-946
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• 2013

The quantitative analysis of evapotranspiration (ET) is a key component in hydrological studies and the establishment of water resources planning. Generally, the quantitative analysis of ET is performed by the estimation method of potential or reference ET based on meteorological factors such as air temperature, wind speed, etc. Hargreaves equation is one of empirical methods for reference ET using air temperature data. In this study, in order to estimate more exact reference ET considering climatological characteristics in Korea, parameter regionalization of Hargreaves equation is carried out. Firstly, modified Hargreaves equation is presented after the analysis of the relationship between solar radiation and temperature. Secondly, parameter ($K_{ET}$) optimization of Hargreaves equation is performed using Penman-Monteith method and modified equation at 71 weather stations. Lastly, the equation for calculating $K_{ET}$ using temperature data is proposed and verified. As a result, reference ET from original Hargreaves equation is overestimated or underestimated compared with Penman-Monteith method. But modified equation in this study is more accurate in the climatic conditions of Korea. In addition, the applicability of the equation between $K_{ET}$ and temperature is confirmed.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.35-38
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• 2004

We developed a Near Real-Time Validation System (NRVS) for the Level-2 Products of AQUA Satellite. AQUA satellite is the second largest project of Earth Observing System (EOS) mission of NASA. This satellite provides the information of water cycle of the entire earth with many different forms. Among its products, we have used five kinds of level-2 geophysical parameters containing rain rate, sea surface wind speed, skin surface temperature, atmospheric temperature profile, and atmospheric humidity profile. To use these products in a scientific purpose, reasonable quantification is indispensable. In this paper we explain the near real-time validation system process and its detail algorithm. Its simulation results are also analyzed in a quantitative way. As reference data set in-situ measured meteorological data which are periodically gathered and provided by the Korea Meteorological Administration (KMA) is processed. Not only site-specific analysis but also time-series analysis of the validation results are explained and detail algorithms are described.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.2
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• pp.244-253
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• 2022

Recently, installation projects of structures such as offshore wind farms have been increasing, and the installation of such marine obstacles could affect ships that pass nearby. Therefore, the purpose of this study was to quantitatively evaluate the risk posed to passing ships due to obstacles in their passage. Hence, parameters that affected the risk were selected, and scenarios were set based on the parameters. The scenarios were evaluated through the ES model, which is a risk assessment model, and we confirmed that the risk ratio increased as the size of the obstacle increased, the safe distance from the obstacle increased, the speed of ship decreased, and the traffic volume increased. Additionally, we found that when the traffic flow direction was designated, the risk ratio was lower than that of general traffic flow. In this study, we proposed a generalization model based on the results of the performed scenarios, applied it to the Dadaepo offshore wind farm, and demonstrated that the estimation of the approximate risk ratio was possible through the generalization model. Finally, we judged that the generalization model proposed in this study could be used as a preliminary reference for the installation of marine obstacles.

• Journal of the Korean earth science society
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• v.42 no.5
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• pp.514-523
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• 2021

Using the precipitation data observed at the Gochang Standard Weather Observatory (GSWO) during the winter seasons from 2014 to 2016, we analyzed the precipitation characteristics of the winter observation environment. For this study, we used four different types of precipitation gauges, i.e., No Shield (NS), Single Alter (SA), Double Fence Intercomparison Reference (DFIR), and Pit Gauge (PG). We analyzed the data from each to find differences in the accumulated precipitation, characteristics of the precipitation type, and the catch efficiency according to the wind speed based on the DFIR. We then classified these into three precipitation types, i.e., rain, mixed precipitation, and snow, according to temperature data from Gochang's Automated Synoptic Observing System (ASOS). We considered the DFIR to be the standard precipitation gauge for our analysis and the cumulative winter precipitation recorded by each other gauge compared to the DFIR data in the following order (from the most to least similar): SA, NS, and PG. As such, we find that the SA gauge is the most accurate when compared to the standard precipitation gauge used (DFIR), and the PG system is inappropriate for winter observations.

• Journal of Bio-Environment Control
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• v.25 no.4
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• pp.270-276
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• 2016

This study analysed overall heat transfer coefficient, heat transmission, and rate of indoor air heating provided by water curtain in order to determine the heat transfer characteristic of double-layered greenhouse equipped with a water curtain system. The air temperatures between the inner and outer layers were determined by the water flow rate and inlet water temperature. Higher water flow rate and inlet water temperature resulted in the increased overall heat transfer coefficient between indoor greenhouse air and water curtain. However, it was found that with higher levels of water flow rate and inlet water temperature, indoor overall heat transfer coefficient was converged about $10W{\cdot}m^{-2}{\cdot}^oC^{-1}$. The low correlation of overall heat transfer coefficient between water curtain and air within double layers was likely because the combination of greenhouse shape, wind speed and outdoor air temperature as well as water curtain affected the heat transfer characteristics. As water flow rate and inlet water temperature increased, the heat transferred into the greenhouse by water curtain also tend to rise. However it was demonstrated that the rate of heat transmission from water curtain into greenhouse with water curtain system using underground water was accounted for 22% to 28% for total heat lost by water curtain. The results of this study which quantify heat transfer coefficient and net heat transfer from water curtain may be a good reference for economical design of water curtain system.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.491-499
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• 2018

Runway visual range (RVR), one of the important indicators of aircraft takeoff and landing, is affected by meteorological conditions such as temperature, humidity, etc. It is important to estimate the RVR at the time of arrival in advance. This study estimated the RVR of the local airport after 1 hour by upgrading the RVR estimation model using the proposed deep learning network. To this end, the advancement of the estimation model was carried out by changing the time interval of the meteorological data (temperature, humidity, wind speed, RVR) as input value and the linear conversion of the results. The proposed method generates estimation model based on the past measured meteorological data and estimates the RVR after 1 hour and confirms its validity by comparing with measured RVR after 1 hour. The proposed estimation model could be used for the RVR after 1 hour as reference in small airports in regions which do not forecast the RVR.

• Atmosphere
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• v.29 no.3
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• pp.269-282
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• 2019

While it is important to obtain the accurate information on snowfall data due to the increase in damage caused by the heavy snowfall in the winter season, it is not easy to observe the snowfall quantitatively. Recently, snow measurements using a weighing precipitation gauge have been carried out, but there is a problem that high snowfall intensity results in low accuracy. Also, the observed snowfall data are sensitive depending on wind speed, temperature, and humidity. In this study, a new process of quality control for snow water equivalent (SWE) data of the weighing precipitation gauge were proposed to cover the low accuracy of snow data and maximize the data utilization. Snowfall data (SWE) observed by Pluvio, Parsivel, snow-depth meter using laser or ultrasonic, and rainfall gauge in Cloud Physics Observation Site (CPOS) were compared and analyzed. Applying the QC algorithm including the use of number of hydrometeor particles as reference, the increased SWE per the unit time was determined and the data noise was removed and marked by flag. The SWE data converted by the number concentration of hydrometeor particles are tested as a method to restore the QC-removed data, and show good agreement with those of the weighing precipitation gauge, though requiring more case studies. The three events data for heavy snowfall disaster in Pyeongchang area was analyzed. The SWE data with improved quality was showed a good correlation with the eye-measured data ($R^2$ > 0.73).

• Membrane and Water Treatment
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• v.10 no.1
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• pp.1-11
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• 2019

The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) predicted that recent extreme hydrological events would affect water quality and aggravate various forms of water pollution. To analyze changes in water quality due to future climate change, input data (precipitation, average temperature, relative humidity, average wind speed and sunlight) were established using the Representative Concentration Pathways (RCP) 8.5 climate change scenario suggested by the AR5 and calculated the future runoff for each target period (Reference:1989-2015; I: 2016-2040; II: 2041-2070; and III: 2071-2099) using the semi-distributed land use-based runoff processes (SLURP) model. Meteorological factors that affect water quality (precipitation, temperature and runoff) were inputted into the multiple linear regression analysis (MLRA) and artificial neural network (ANN) models to analyze water quality data, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (T-N) and total phosphorus (T-P). Future water quality prediction of the Anseongcheon River basin shows that DO at Gongdo station in the river will drop by 35% in autumn by the end of the $21^{st}$ century and that BOD, COD and SS will increase by 36%, 20% and 42%, respectively. Analysis revealed that the oxygen demand at Dongyeongyo station will decrease by 17% in summer and BOD, COD and SS will increase by 30%, 12% and 17%, respectively. This study suggests that there is a need to continuously monitor the water quality of the Anseongcheon River basin for long-term management. A more reliable prediction of future water quality will be achieved if various social scenarios and climate data are taken into consideration.

• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.