• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.23 no.5
• /
• pp.345-357
• /
• 2011

Recently, as part of diversifying energy sources and earth environmental issues, technology development of new renewable energy using wave energy is actively promoted and commercialized around Europe and Japan etc. In particular, OWC(Oscillating Water Column) wave power generation system using air flow induced by vertical movement of the water surface by waves in an air-chamber within caisson is known as the most efficient wave energy absorption device and therefore, is one of the wave power generation apparatus the closest to commercialization. This study examines air flow velocity, which operates turbine(Wells turbine) directly in oscillating water column type wave power generation structure from two-and three-dimensional numerical experiments and discusses optimal shape of oscillating water column type wave power generation structure by estimating the maximum flow rate of air according to change in shape. The three-dimensional numerical wave flume was applied in interpretation for this study which is the model for the immiscible two-phase flow based on the Navier-Stokes Equation. From this, it turned out that size of optimal shape appears differently according to the incident wave period and air flow is maximized at the period where minimum reflection ratio occurs.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.47 no.12
• /
• pp.831-840
• /
• 2019

Flush Air Data Sensing system (FADS) estimates air data states using pressure data measured at the surface of flight vehicles. The FADS system does not require intrusive probes, so it is suitable for high performance aircrafts, stealth vehicles, and hypersonic flight vehicles. In this study, calibration procedures and solution algorithms of the FADS for a sphere-cone shape vehicle are presented for the prediction of air data from subsonic to supersonic flights. Five flush pressure ports are arranged on the surface of nose section in order to measure surface pressure data. The algorithm selects the concept of separation for the prediction of flow angles and the prediction of pressure related variables, and it uses the pressure model which combines the potential flow solution for a subsonic flow with the modified Newtonian flow theory for a hypersonic flow. The CFD code which solves Euler equations is developed and used for the construction of calibration pressure data in the Mach number range of 0.5~3.0. Tests are conducted with various flight conditions for flight Mach numbers in the range of 0.6~3.0 and flow angles in the range of -10°~+10°. Air data such as angle of attack, angle of sideslip, Mach number, and freestream static pressure are predicted and their accuracies are analyzed by comparing predicted data with reference data.

• Atmosphere
• /
• v.30 no.2
• /
• pp.183-193
• /
• 2020

In this study, Yeongdong cold air damming (YCAD) cases that occur in winters have been selected using automatic weather station data of the Yeongdong region of Korea. The vertical and horizontal scales of YCAD were analyzed using rawinsonde and numerical weather model. YCAD occurred in two typical synoptic patterns such that low pressure and trough systems crossing and passing over Korea (low crossing type: LC and low passing type: LP). When the Siberian high does not expand enough to the Korean peninsula, low pressure and trough systems are likely to move over Korea. Eventually this could lead to surface temperature (3.1℃) higher during YCAD than the average in the winter season (1.6℃). The surface temperature during YCAD, however, was decrease by 1.3℃. The cold air layer was elevated around 120 m~450 m for LP-type. For LC-type, the cold layer were found at less than approximately 400 m and over 1,000 m, which could be thought of combined phenomena with synoptic and local weather forcing. The cross-sectional analysis results indicate the accumulation of cold air on the east mountain slope. Additionally, the north or northeasterly winds turned to the northwesterly wind near the coast in all cases. The horizontal wind turning point of LC-type was farther from the top of the mountain (52.2 km~71.5 km) than that of LP-type (20.0 km~43.0 km).

• Journal of Climate Change Research
• /
• v.3 no.2
• /
• pp.153-159
• /
• 2012

CFC-12 used in mobile air conditioning(MAC) system has been replaced by R-134a, a type of HFC refrigerant, from 1991 to 1994. R-134a has since been widely used as a refrigerant of a mobile air conditioner. However, it is one of the six main green house gases listed in Kyoto Protocol, which makes it imperative to regulate its emission and develop alternative refrigerants. In this study, the concentration of leaked R-134a was measured using VT(Variable Temperature) shed and Running loss test shed to analyze the level of air conditioner refrigerant leaked in a vehicle. According to the analysis of the concentration of R-134a leaked from a vehicle parked, annual leakage amount of R-134a was in the range of 6.46~13.28 g/yr. The figure was similar with the leakage from the mobile air conditioning system currently used. In a study using the same vehicle model, a vehicle equipped with dual evaporation system had a higher leakage rate of refrigerant than a vehicle with a single evaporation system. It appears that the added fittings and joints of the dual evaporator system led to higher leakage rate. Besides, the analysis of the change in R-134a concentration under various car speed found that more refrigerant leaked under high speed(100km/hr) and but the volume of the wind did not affect to the variation of refrigerant leakage.

• Atmosphere
• /
• v.30 no.4
• /
• pp.377-390
• /
• 2020

Not only emissions, but also atmospheric circulation is a key factor that affects local particulate matters (PM) concentrations in Korea through ventilation effects and transboundary transports. As part of the atmospheric circulation, air stagnation especially adversely affects local air quality due to weak ventilation. This study investigates the large-scale circulation related to air stagnation over Korea during winter and projects the climate change impacts on atmospheric patterns, using observed PM data, reanalysis and regional climate projections from HadGEM3-RA with Modified Korea Particulate matter Index. Results show that the stagnation affects the PM concentration, accompanied by pressure ridge at upper troposphere and weaken zonal pressure gradient at lower troposphere. Downscaling using HadGEM3-RA is found to yield Added-Value in the simulated low tropospheric winds. For projection of future stagnation, SSP5-8.5 and SSP1-2.6 (high and low emission) scenarios are used here. It has been found that the stagnation condition occurs more frequently by 11% under SSP5-8.5 and by 5% under SSP1-2.6 than in present-day climate and is most affected by changes in surface wind speed. The increase in the stagnation conditions is related to anticyclonic circulation anomaly at upper troposphere and weaken meridional pressure gradient at lower troposphere. Considering that the present East Asian winter monsoon is mainly affected by change in zonal pressure gradient, it is worth paying attention to this change in the meridional gradient. Our results suggest that future warming condition increase the frequency of air stagnation over Korea during winter with response of atmospheric circulation and its nonlinearity.

• Korean Chemical Engineering Research
• /
• v.58 no.4
• /
• pp.588-595
• /
• 2020

Prediction and control of nitrogen oxides (NO_x) emission is of great interest in industry due to stricter environmental regulations. Herein, we propose an artificial intelligence (AI)-based framework for prediction of NO_x emission. The framework includes pre-processing of data for training of neural networks and evaluation of the AI-based models. In this work, Long-Short-Term Memory (LSTM), one of the recurrent neural networks, was adopted to reflect the time series characteristics of NO_x emissions. A decision tree was used to determine a time window of LSTM prior to training of the network. The neural network was trained with operational data from a heating furnace. The optimal model was obtained by optimizing hyper-parameters. The LSTM model provided a reliable prediction of NO_x emission for both training and test data, showing an accuracy of 93% or more. The application of the proposed AI-based framework will provide new opportunities for predicting the emission of various air pollutants with time series characteristics.

• Journal of the Korean Institute of Gas
• /
• v.2 no.4
• /
• pp.60-66
• /
• 1998

The hot stove system is a process that is continuously and constantly generating the hot combustion air required for the blast furnace. The hot stove process is considered as a main energy consumption process because it consumes about $20\%$ of the total energy in steel making works. So, many researchers have interested in the improvement of the heat efficiency of the hot stove to reduce the energy consumption. But they have difficulties in improving the heat efficiency of the hot stove because there is no precise information on heat transformation occurring during the heating period. In order to model the relationship between the operating conditions and heat efficiencies, we propose a neural network using feature extraction as one of experimental modeling methods. In order to show the performance of the model, we compare it with Partial Least Square (PLS) method. Both methods have similarities in using the dimension reduction technique. And then we present the simulation results on the prediction of the heat efficiency of the hot stove.

• Journal of the Korean earth science society
• /
• v.30 no.3
• /
• pp.294-304
• /
• 2009

A statistical prediction model for the typhoon intensity and track in the Northwestern Pacific area was developed based on the artificial neural network scheme. Specifically, this model is focused on the 5-day prediction after tropical cyclone genesis, and used the CLIPPER parameters (genesis location, intensity, and date), dynamic parameters (vertical wind shear between 200 and 850hPa, upper-level divergence, and lower-level relative vorticity), and thermal parameters (upper-level equivalent potential temperature, ENSO, 200-hPa air temperature, mid-level relative humidity). Based on the characteristics of predictors, a total of seven artificial neural network models were developed. The best one was the case that combined the CLIPPER parameters and thermal parameters. This case showed higher predictability during the summer season than the winter season, and the forecast error also depended on the location: The intensity error rate increases when the genesis location moves to Southeastern area and the track error increases when it moves to Northwestern area. Comparing the predictability with the multiple linear regression model, the artificial neural network model showed better performance.

• Journal of Advanced Navigation Technology
• /
• v.23 no.6
• /
• pp.597-604
• /
• 2019

Artificial neural networks are algorithms that simulate learning through interaction and experience in neurons in the brain and that are a method that can be used to produce accurate results through learning that reflects the characteristics of data. In this study, a model using deep neural network was presented to improve the predicted wind speed values in the meteorological dynamic model. The wind speed prediction improvement model using the deep neural network presented in the study constructed a model to recalibrate the predicted values of the meteorological dynamics model and carried out the verification and testing process and Separate data confirm that the accuracy of the predictions can be increased. In order to improve the prediction of wind speed, an in-depth neural network was established using the predicted values of general weather data such as time, temperature, air pressure, humidity, atmospheric conditions, and wind speed. Some of the data in the entire data were divided into data for checking the adequacy of the model, and the separate accuracy was checked rather than being used for model building and learning to confirm the suitability of the methods presented in the study.

• Proceedings of the Korea Air Pollution Research Association Conference
• /
• 1998.10a
• /
• pp.83-92
• /
• 1998

오존 경보제와 예보제의 실시가 오존 오염에 관한 일반인의 관심을 증폭시키는 중요한 계기가 되었음에도 우리 연구계와 정책 당국의 대응은 이에 훨씬 못미치고 있다. 1990년 이래 학회지 관련 논문 편수는 연간 1∼3편에 그치고 있고, 다른 이차오염 현상과 달리 정책 당국은 여전히 지원을 고려하지 않고 있다. 오존 연구가 뿌리를 내리지 못한 가운데 강행된 경보제와 예보제의 와중에 책임을 떠맡은 지방자치단체는 조급하게 노련한 예보 모델을 찾고 있고 충분한 검토도 없이 외국의 사례에서 대책을 구하고 있다. 논란이 되고 있는 오존 예보의 부정확성은 모델 선택의 문제가 아니라 모델 이용이 잘못된 때문이다. 현재 오존 대책의 일환으로 검토되고 있는 차량 부제 실시와 같은 단기 대책은 역효과를 낼 수 있다는 연구결과도 있다. 선진국의 오존 연구는 도시 규모에서 지역 규모로 확대되고 있고, 구츰 등 액상반응의 효과 규명과 1 kin 이하 미세 변화 모델링이 시도되고 있고, 지구 단위 대류권 실험의 일환으로 태평양 상공의 오존과 오존 전구물질의 대기화학이 조사되고 있다. 우리는 우선 우리의 오존 문제를 정확히 이해하도록 노력하여야 한다 합리적 계획 아래 체계적으로 접근하여 갈 때 당장은 아니지만 우리의 오존에 대한 이해가 깊어갈수록 예보의 정확성은 향상될 수 있다. 많은 비용과 노력이 필요한 오존 대책의 시행은 충분히 신중하여야 한다. 서울과 같은 대도시라면 적합한 수치모델을 구비하여 효과를 점검할 수 있어야 한다. 일부에서는 예보의 정확성을 높일 수 있는 방안의 하나로 수치모델 이용을 거론하고 있으나 수치모델은 매일의 예보와 같이 일상적 목적을 위하여 사용될 수 있는 모델이 아니며, 현재와 같이 기초가 갖추어져 있지 않은 상황에서는 더욱 그러하다. 정상적 모델 이용이 가능할 수 있도록 배출원 자료 체계를 갖추어야 하고, 서을 등 특징적 지역에 대하여서는 집중적 현장 조사를 실시하여야 한다.3)와 NAS(National Academy of Sciences, 1983), 미국에서 발행되는 정부 지침서 (Federal Register)에 고시된 내용 등을 토대로 하였다. 연구는 당면현실로 다가온 정보화 및 세계화의 기업환경에서 예견되는 몰입(committment)의 약화에 대한 치유방안으로서, 정보화된 경영모의게임의 기업 교육훈련 시스템(Business Training System)으로의 발전 가능성을 제시한다 하겠다.암시하며, 따라서 우리 교육문화에 맞는 재택수업 형태의 개발이 시급함을 제시한다고 하겠다.column density of HCaN is (1-3):n1014cm-2. Column density at distant position from MD5 is larger than that in the (:entral region. We have deduced that this hot-core has a mass of 10sR1 which i:s about an order of magnitude larger those obtained by previous studies.previous studies.업순서들의 상관관계를 고려하여 보다 개선된 해를 구하기 위한 연구가 요구된다. 또한, 준비작업비용을 발생시키는 작업장의 작업순서결정에 대해서도 연구를 행하여, 보완작업비용과 준비비용을 고려한 GMMAL 작업순서문제를 해결하기 위한 연구가 수행되어야 할 것이다.로 이루어 져야 할 것이다.태를 보다 효율적으로 증진시킬 수 있는 대안이 마련되어져야 한다고 사료된다.$\ulcorner$순응$\lrcorner$의 범위를 벗어나지 않는다. 그렇기 때문에도 $\ulcorner$순응$\lrcorner$과 $\ulcorner$표현$\lrcorner$의 성격과 형태를 외형상으로 더욱이 공간