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충격기류식 여과집진기의 내부 유동 시뮬레이션 해석을 통한 압력손실 예측
장경민,정은상,서정민,Jang, Kyeong-Min,Jung, Eun-Sang,Suh, Jeong-Min 한국환경과학회 2020 한국환경과학회지 Vol.29 No.5
With continuous industrial development, the types, and amount of particulate matter (PM) have been increasing. Since 2018, environmental standards regarding PM have become more stringent. Pulse air jet bag filters are suitable for PM under the 20 ㎛ and, can function regardless of size, concentration and type. Filtration velocity and shape are important factors in the operation and design of the pulse air jet bag filters however, few established studies support this theory. In this research, numerical simulations were conducted based on experimental values and, several methods were employed for minimizing the pressure drop. In the pilot system, as the inlet duct velocity was faster than 19 m/sec, flow was not distributed equally and, re-entrainment occurred due to the hopper directional vortex. The multi-inlet system decelerated the hopper directional vortex by 25 ~ 30%, thereby decreasing total pressure drop by 6.6 ~ 14.7%. The guide vane system blocked the hopper directional vortex, which resulted optimal vane angle of 53°. The total pressure of the guide vane system increased by 0.5 ~ 3% at 1.5 m/min conditions. However, the filtration pressure drop decreased by 4.8 ~ 12.3% in all conditions, thereby reducing the operating cost of filter bags.
유도 전극과 가속 전극의 형태와 조합에 따른 EHD 특성 연구
장경민(Kyeong-Min Jang),김철규(Chol-Gyu Kim),김진규(Jin-Gyu Kim) 한국조명·전기설비학회 2019 조명·전기설비학회논문지 Vol.33 No.5
The electrodynamics(EHD) ionic wind cooling system is very efficient in cooling of electronics, compared to the standard cooling systems. However, standard ionic wind generator is only composed of a corona electrode and an induction electrode, to be limited in improving the ionic wind velocity. In order to improve the ionic wind velocity, we applied accelerating electrode to a conventional ionic wind generator. and investigated the effect of change in shapes of induction and accelerating electrode pairs. Applying accelerating (mesh or ring) electrodes to a shape of corona (needle) electrodes and induction (mesh or ring) electrodes in an ionic wind generator increase the ionic wind velocity according to application of voltage to the accelerating electrodes. Also, increase rate of ionic wind velocity was higher when the induction electrodes were rings and the accelerating electrodes were mesh.
장경민 ( Kyeong-min Jang ),배석환 ( Seok-hwan Bae ),신창선 ( Chang-sun Shin ),박장우 ( Jang-woo Park ),조용윤 ( Young-yoon Cho ) 한국정보처리학회 2016 한국정보처리학회 학술대회논문집 Vol.23 No.1
일반적으로 공공기관 혹은 다수의 이용자가 사용하는 화장실은 관리 상황은 열악한 편이다. 화장실을 관리하는 곳도 있지만 상황은 그다지 다르지 않다. 그래서 사용하는 사람과 관리하는 사람 모두에게 이로운 자동 물 내림 변기가 필요하다고 생각한다. 이미 시중에 나와 있는 많은 자동 물 내림 변기들이 있다. 하지만 공공화장실에서는 가격 등의 제약조건에 의해 많이 사용되지 않고 있는 실정이다. 본 연구에서는 아두이노와 pH센서를 이용하여 기존의 자동 물내림 변기보다 간단하고 저렴한 자동 물 내림 변기를 구현하였다.
장경민 ( Jang Kyeong Min ),이명배 ( Lee Myeong Bae ),임종현 ( Lim Jong Hyun ),오한별 ( Oh Han Byeol ),신창선 ( Shin Chang Sun ),박장우 ( Park Jang Woo ) 한국정보처리학회 2023 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.12 No.3
In this study, we compared the performance of machine learning models for predicting Vapor Pressure Deficits (VPD) in greenhouses that affect pore function and photosynthesis as well as plant growth due to nutrient absorption of plants. For VPD prediction, the correlation between the environmental elements in and outside the greenhouse and the temporal elements of the time series data was confirmed, and how the highly correlated elements affect VPD was confirmed. Before analyzing the performance of the prediction model, the amount and interval of analysis time series data (1 day, 3 days, 7 days) and interval (20 minutes, 1 hour) were checked to adjust the amount and interval of data. Finally, four machine learning prediction models (XGB Regressor, LGBM Regressor, Random Forest Regressor, etc.) were applied to compare the prediction performance by model. As a result of the prediction of the model, when data of 1 day at 20 minute intervals were used, the highest prediction performance was 0.008 for MAE and 0.011 for RMSE in LGBM. In addition, it was confirmed that the factor that most influences VPD prediction after 20 minutes was VPD (VPD_y__71) from the past 20 minutes rather than environmental factors. Using the results of this study, it is possible to increase crop productivity through VPD prediction, condensation of greenhouses, and prevention of disease occurrence. In the future, it can be used not only in predicting environmental data of greenhouses, but also in various fields such as production prediction and smart farm control models.