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인공지능을 이용한 이종액체 정상 상태 혼합의 혼합과정 해석

Analyses of Steady State Mixing Process of Two-Liquids Using Artificial Intelligence

  • 공대경 (한국해양대학교 대학원 냉동공조공학과) ;
  • 염주호 (한국해양대학교 대학원 냉동공조공학과) ;
  • 조경래 (한국해양대학교 기계공학부) ;
  • 도덕희 (한국해양대학교 기계공학부)
  • KONG, DAEKYEONG (Division of Refrigeration & Air-conditioning Eng., Graduate School of Korea Maritime & Ocean University) ;
  • YUM, JUHO (Division of Refrigeration & Air-conditioning Eng., Graduate School of Korea Maritime & Ocean University) ;
  • CHO, GYEONGRAE (Division of Mechanical Eng., Korea Maritime & Ocean University) ;
  • DOH, DEOGHEE (Division of Mechanical Eng., Korea Maritime & Ocean University)
  • 투고 : 2018.09.03
  • 심사 : 2018.10.30
  • 발행 : 2018.10.30

초록

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.

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참고문헌

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