• Journal of Power System Engineering
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• v.16 no.3
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• pp.37-43
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• 2012

감온액정을 추적입자로 사용한 PIV(Particle Image Velocimetry)법이 온도장을 정량적으로 가시화하기 위하여 사용되었다. 이 방법은 전체 유동장과 온도장을 동시에 계측할 수 있는 방법이지만, 온도장의 온도는 온도에 따라 변화되는 액정의 색을 정량적 온도 값으로 변환시켜야한다. 따라서 본 연구에서는 감온액정에 의한 온도장의 광학적 정보를 정량적 온도로 변환시키는 신경회로망 보정기법을 개발하여 그 타당성을 검토한 후, 수직온도구배를 가진 액체의 기포에 의한 대류유동에 적용하여 기포에 의한 온도혼합과정을 정량적으로 가시화하고자 한다.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.3
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• pp.395-404
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• 2003

Quantitative data of the temperature and velocity were obtained simultaneously by using liquid crystal tracer. PIV(Particle Image Velocimety) based on a grey-level cross-correlation method was used for visualizing and analysis of the flow field. The temperature gradient was obtained by applying the color-image processing to a visualized image, and a neural-network a1gorithm was applied to the color-to-temperature calibration. This simultaneous measurement was applied to the Rayleigh-Bernard convection. This paper describes the method, and presents the quantitative visualization of Rayleigh-Bernard convection and the effect of aspect ratio and viscosity. Also the experimental results were compared with the numerical results.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.11
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• pp.1572-1578
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• 2003

Particle Image Thermometry(PIT) with liquid crystal tracers is used for visualizing and analysis of the bubbly flow in a vertical temperature gradient. Quantitative data of the temperature were obtained by applying the color-image processing to a visualized image, and neural-network was applied to the color-to-temperature calibration. This paper describes the method, and presents the transient mixing temperature patterns of the bubbly flow.

• 한국가시화정보학회:학술대회논문집
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• 2004.11a
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• pp.52-55
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• 2004

Thermo-chromic Liquid Crystal(TLC) particles were used as temperature sensor for thermal fluid flow. $1K\times1K$ CCD color camera and Xenon Lamp(500W) were used for the visualization of a Hele-Shaw cell. The characteristic between the reflected colors from the TLC and their corresponding temperature shows strong non-linearity. A neural network known as having strong mapping capability for non-linearity is adopted to quantify the temperature field using the image of the flow. Improvements of color-to-temperature mapping was attained by using the local color luminance (Y) and hue (H) information as the inputs for the constructed neural network.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.2
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• pp.209-216
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• 2005

Thermo-chromic Liquid Crystal(TLC) particles were used as temperature sensor for thermal fluid flow. 1K $\times$ 1K CCD color camera and Xenon Lamp(500w) were used for the visualization of a Hele-Shaw cell The characteristic between the reflected colors from the TLC and their corresponding temperature shows strong non-linearity A neural network known as having strong mapping capability for non-linearity is adopted to quantify the temperature field using the image of the flow. Improvements of color-to-temperature mapping was attained by using the local color luminance (Y) and hue (H) information as the inputs for the constructed neural network.

• Journal of Power System Engineering
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• v.9 no.2
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• pp.33-39
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• 2005

Experiments are performed to study the mixed convection flow and heat transfer in an inclined parallel plates with the upper part cooled and the lower part heated uniformly. The Reynolds number ranges from $4.0{\times}10^{-3}\;to\;6.2{\times}10^{-2}$, the angle of inclination, ${\theta}$, from 0 to 45 degree from the horizontal line, and Pr of the high viscosity fluid is 909. In this paper, the PIV(Particle image velocimetry) with TLC(Thermo-sensitive liquid crystal) tracers is used for visualizing and analysis. This method allows simultaneous measurement of velocity and temperature field at a given instant of time. Quantitative data of the temperature and velocity are obtained by applying the color-image processing to a visualized image, and neural network is applied to the color-to-temperature calibration. This paper describes the methods, and presents the quantitative visualization of mixed convection. From this study, it is found that the flow pattern can be classified into three patterns which are affected by Reynolds number and the angle of inclination.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.18 no.9
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• pp.698-706
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• 2006

Experiment and numerical calculation have been peformed to investigate mixed convection heat transfer between inclined parallel plates. Particle image velocimetry (PIV) with thermo-sensitive liquid crystal (TLC) tracers is used for visualizing and analysis. This method allows simultaneous measurement of velocity and temperature fields at a given instant of time. Quantitative data of the temperature and velocity are obtained by applying the color-image processing to a visualized image, and neural network is applied to the color-to-temperature calibration. The governing equations are discretized using the finite volume method. The results are presented for the Reynolds number ranges from 0.004 to 0.062, the angle of inclination, ${\Theta}$, from 0 to 45 degree and Prandtl number of the high viscosity fluid is 909. The results show velocity, temperature and mean Nusselt numbers distributions. It is found that the periodic flow of mixed convection between inclined parallel plates is shown at $0^{\circ}{\leq}{\Theta}<30^{\circ}$, Re<0.062, and the flow pattern can be classified into three patterns which depend on Reynolds number and the angle of inclination. The minimum Nusselt numbers occur at Re=0.05 regardless of the angle of inclination.