Video measurement based quantitative background schlieren method

Abstract

The invention discloses a video measurement (VM) based quantitative background schlieren (BOS) method. A VM technology and a BOS light path are combined, and small evenly distributed round points are adopted as BOS background points, so that on one hand, a VM mature round marking point image processing technology is utilized to ensure the point positioning accuracy of small round point imaging is up to 0.02 pixels; on the other hand, the defection displacements produced by light beams starting from the small background round points to the photographing center of a camera and penetrating through a flow field on a background board are accurately calculated through a collinearity equation, an exact defection angle calculation formula is given to accurately obtain the defection angles and light path differences of all the background point positions, accordingly the errors produced by an existing BOS adopting a parallel light deflection angle calculation formula are eliminated, and the adverse effect on the limit of existing BOS measurement and BOS measurement accuracy of an image mutual-correlation technology is overcome.

Description

technical field [0001] The invention relates to the technical field of flow display and aero-optical testing, in particular to a quantitative background schlieren method based on video measurement. Background technique [0002] Schlieren technology has existed as a flow display method for nearly a hundred years. However, in practical applications, schlieren is usually only used for qualitative observation and analysis, and is rarely used as a quantitative test method. There are two main reasons for this: [0003] 1) There are many factors that affect the gray scale of the schlieren image, such as light source intensity, glass transmittance, thickness of the test area, etc., making it difficult to establish a quantitative relationship between the image and the physical quantity to be measured; [0004] 2) The schlieren image reflects the first-order derivative field of the refractive index, and it is difficult to extract data. [0005] For this reason, the existing backgrou...

AI Technical Summary

Problems solved by technology

[0003] 1) There are many factors that affect the gray scale of the schlieren image, such as light source intensity, glass transmittance, thickness of the test area, etc., making it difficult to establish a quantitative relationship between the image and the physical quantity to be measured;

[0004] 2) The schlieren image reflects the first-order derivative field of the refractive index, and it is difficult to extract data

The reason is that: if figure 2 As shown, for a background spot A at a given position, the ray starting from point A to the photographic center O of the camera The imaging point on the CCD is a, obviously Not parallel to the z-axis, when using the small deflection angle calculation formula of parallel light to get The error is obvious, especially when the background spot A is farther away from the z-axis, the greater the error

[0010] 2) The light deflection displacement at a given point cannot be accurately obtained: because the PIV technology is used to obtain the light deflection displacement of the background spot on the imaging plane, that is, the displacement of the entire window is obtained by obtaining the cross-correlation of the iterative query window The amount of change, therefore, it obtains the average displacement of the entire iteration window

[0011] 3) The size of the iterative query window has a great influence on the accuracy of the existing BOS: if the image cross-correlation window is too small, the correct offset will not be obtained, and if the window is too large, the accuracy of the offset will be reduced, especially when When the flow changes drastically, such as when the beam passes through the supersonic shock wave, the background spot at the shock wave has a large deflection displacement mutation range, and the PIV image cross-correlation window method to calculate the offset will fail due to the sharp image change, and the robustness is poor

[0012] 4) The existing BOS uses a random image as the background. If there is a spatially periodic structure in the background image, the PIV algorithm will not be able to correctly interpret the background displacement vector

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