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정량적 유동가시화 기술을 이용한 이종연료유 과도 혼합 농도분포 측정
염주호,도덕희,조경래,민성기,김명호,유경원 한국수소및신에너지학회 2012 한국수소 및 신에너지학회논문집 Vol.23 No.4
Transient mixing states of two different fuel oils, dimethylformamide (DMF) oil and JetA1 oil, were investigated by using a color image processing and a neural network. A tank (D × H, 310 × 370 mm) was filled with JetA1 oil. The DMF oil was filled at a top tank, and was mixed with the JetA1 oil in the tank mixing tank via a sudden opening which was performed by nitrogen gas with 1.9 bar. An impeller was rotated with 700 rpm for mixing enhancements of the two fuel oils. To visualize the mixing state of the DMF oil with the JetA1 oil, the DMF oil was coated with Rhodamine B whose color was red. A LCD monitor was used for uniform illumination. The color changes of the DMF oil were captured by a camcoder and the images were transferred to a host computer for quantifying the information of color changes. The color images of two mixed oils were captured with the camcoder. The R, G, B color information of the captured images was used to quantify the concentration of the DMF oil. To quantify the concentration of the DMF oil in the JetA1 oil, a calibration of color-to-concentration was carried out before the main experiment was done. Transient mixing states of DMF oil with the JetA1 oil since after the sudden infiltration were quantified and characterized with the constructed visualization technique.
도덕희,염주호,조경래,민성기,김명호,유경원,유남현 한국수소및신에너지학회 2012 한국수소 및 신에너지학회논문집 Vol.23 No.6
Concentration fields of solid powder in a liquid fuel were quantitatively measured by a visualization technique. The measurement system consists of a camcoder and three LCD monitors. The solid powder (glass powder) were filled in a head tank which was installed over a main mixing tank (D x H, 310 x 370 mm). The main mixing tank was filled with JetA1 fuel oil. With a sudden opening of the upper tank by pressurized nitrogen gas with 1.9 bar, the solid powder were poured into the JetA1 oil. An impeller type agitator was being rotated in the mixing with 700 rpm for the enhancements of mixing. Uniform visualization for the mixing flow field was made by the light from the three LCD monitors, and the visualized images were captured by the camcoder. The color images captured by the camcoder The color information of the captured images was decoded into three principle colors R, G, and B to get quantitattive relations between the concentrations of the solid powder and the colors. To get better fitting for the strong non-linearity between the concentration and the color, a neural network which has strong fitting performances was used. Analyses on the transient mixing of the solid powders were quantitatively made.
인공지능을 이용한 이종액체 정상 상태 혼합의 혼합과정 해석
공대경,염주호,조경래,도덕희 한국수소및신에너지학회 2018 한국수소 및 신에너지학회논문집 Vol.29 No.5
Two liquids which are generally used as fuels of rockets are mixed and their mixing process is quantitatively investigated by the use of particle image velocimetry (PIV). As working fluids for the liquid mixing, Dimethylfuran (DMF) and JetA1 oils have been used. Since the specific gravity of DMF is larger than that of JetA1 oil, the DMF oil has been set at the lower part of the JetA1 oil. For better visualization of the mixing process, Rhodamin B powder has been blended into the DMF oil. An agitator having 3 blades has been used for mixing the two liquids. For quantitative visualization, a LCD monitor has been used as a light source. A color camera, camcoder, has been used for recording the mixing process. The images captured by the camcoder have been digitized into three color components, R, G, and B. The color intensities of R, G, and B have been used as the inputs of the neural network of which hidden layer has 20 neurons. Color-to-concentration calibration has been performed before commencing the main experiments. Once this calibration is completed, the temporal changes of the concentration of the DMF has been quantitatively analyzed by using the constructed measurement system.