• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.7
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• pp.864-870
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• 1999

An experimental study Is conducted in a four-vane linear cascade in order to examine the influence of the wake behind rectangular bars on the flow and heat transfer characteristics. Flow and heat transfer measurements are made for the inlet Reynolds number of 66000(based on chord length and free-stream velocity). Turbulent intensity and stress are measured using a hot-wire anemometer, and to measure the convective heat transfer coefficients on the blade surface liquid crystal/gold film Intrex technique is used. Each of experimental cases is characterized by the unsteadiness measured at the entrance of the cascade. The wake behind the rectangular bars enhances the turbulent motion of the flow in the cascade passage. It also promotes the boundary layer development and transition. The results show that heat transfer coefficients on the blade surface increase with increasing unsteadiness.

• 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.