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In this paper the improved Background Oriented Schlieren technique called CBOS (Colored Background Oriented Schlieren) is described and used to reconstruct density fields of three-dimensional flows. The Background Oriented Schlieren technique allows to measure the light deflection caused by density gradients in a compressible flow. For this purpose the distortion of the image of a background pattern observed through the flow is used. In order to increase the performance of the conventional Background Oriented Schlieren technique, the monochromatic background is replaced by a colored dot pattern. The different colors are treated separately using suitable correlation algorithms. Therefore, the precision and the spatial resolution can be highly increased. The CBOS technique is explained and applied to the measurement of the flow field around a free-flight space model. These flow conditions lead to very strong shocks in the front region of the model. Therefore a special arrangement of the different colored dot patterns in the background allows astigmatism in the region with high density gradients to be overcome. An algebraic reconstruction technique (ART), taking into account forward and backward projections, is then used to reconstruct the density field of such flows from CBOS measurements. It could be shown that the accuracy and the spatial resolution of the CBOS technique allows us to obtain a reliable reconstruction of the density field. Especially for complex flows the distribution of the density helps considerably to understand the flow phenomena.
Friedrich Leopold,Daniel Klatt,Masanori Ota, andFrédérique Jagusinki
"Reconstruction of density fields of supersonic flows using an improved Schlieren technique", Proc. SPIE 11160, Electro-Optical Remote Sensing XIII, 1116008 (10 October 2019); https://doi.org/10.1117/12.2533463
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Friedrich Leopold, Daniel Klatt, Masanori Ota, Frédérique Jagusinki, "Reconstruction of density fields of supersonic flows using an improved Schlieren technique," Proc. SPIE 11160, Electro-Optical Remote Sensing XIII, 1116008 (10 October 2019); https://doi.org/10.1117/12.2533463