An object trajectory recognition and shock wave visualization device and method for dropping objects in a wind tunnel

Abstract

An object trajectory recognition and shock wave visualization device and method in a wind tunnel, belonging to the technical field of aerodynamic test and measurement. To achieve the precise recognition of the release trajectory and the combined visualization of shock waves, the present invention includes a square guide rail connected into a double-layer bracket through a wrench slider, placed outside the side wall plate of the wind tunnel test section corresponding to the optical window position. The upper layer of the double-layer bracket places the first imaging device and the second imaging device. The middle of the lower layer of the double-layer bracket places a DC high-power LED light source, and both ends of the lower layer of the double-layer bracket are connected to L-shaped blocks; the computer is respectively connected to the first imaging device and the second imaging device, and the digital pulse signal delay generator is respectively connected to the first imaging device and the second imaging device; a random speckle background is arranged on the inner side of the side wall plate of the wind tunnel test section corresponding to the inner wall of the wind tunnel test section; the release device is connected to the model and placed in the wind tunnel test section for the release of the model. The combined visualization means of the present invention improves the comprehensive analysis ability of wind tunnel experiments.

Description

Technical Field

[0001] The present invention belongs to the technical field of aerodynamic test and measurement, and particularly relates to a device and method for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves. Background Art

[0002] In the modern aviation field, the accuracy of object dropping directly affects the operation effect and system performance. The identification of the dropping trajectory is crucial. Especially in a complex air environment, accurate trajectory identification can help predict the stability and flight characteristics during the dropping process. Wind tunnel tests are an important means to study the aerodynamic characteristics during the dropping process. By simulating the dropping process, the performance and potential problems of an object in a complex flight environment can be effectively evaluated.

[0003] In wind tunnel tests, the shock wave effect accompanying the dropping of an object has an important impact on flight stability and dropping accuracy. Therefore, combining trajectory identification with shock wave visualization can more comprehensively analyze the flow field changes encountered by an object during the dropping process, and thus provide an important basis for the design, optimization, and performance improvement of the aviation dropping system.

[0004] Traditional methods for identifying dropping trajectories mainly rely on feature recognition techniques. These methods analyze by extracting the features of the object's shape or motion trajectory. However, when the object rotates or has a large attitude change, traditional methods are prone to feature loss, resulting in unstable trajectory tracking and a significant decrease in accuracy. On the other hand, traditional shock wave observation methods, such as the schlieren method, although able to effectively capture the dynamics of shock waves, are limited in application in wind tunnel tests. The schlieren method has strict requirements for optical path adjustment, and the equipment has a large volume and high space occupancy, making it difficult to be flexibly arranged in a wind tunnel environment with limited space. Especially in a complex flow field or a narrow space, it is difficult to provide continuous and high-quality shock wave observations. Therefore, existing methods have significant limitations in dropping trajectory identification and shock wave observation, and innovative technologies are needed to break through these bottlenecks. Summary of the Invention

[0005] The problem to be solved by the present invention is to achieve the accurate identification of the dropping trajectory and the joint visualization of shock waves, and to propose a device and method for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves.

[0006] To achieve the above object, the present invention is realized through the following technical solutions:

[0007] A device for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves includes a first imaging device, a second imaging device, an L-shaped block, a wrench slider, a DC high-power LED light source, a computer, a digital pulse signal delay generator, a square guide rail, and a random speckle background;

[0008] The square guide rails are connected into a double-layer bracket through wrench sliders and placed outside the side wall plate of the wind tunnel test section at the position corresponding to the optical window. The first imaging device and the second imaging device are placed on the upper layer of the double-layer bracket, a DC high-power LED light source is placed in the middle of the lower layer of the double-layer bracket, and L-shaped blocks are connected to both ends of the lower layer of the double-layer bracket;

[0009] The computer is respectively connected to the first imaging device and the second imaging device, and the digital pulse signal delay generator is respectively connected to the first imaging device and the second imaging device;

[0010] A random speckle background is arranged on the inner side of the side wall plate of the wind tunnel test section corresponding to the inner wall of the wind tunnel test section;

[0011] The delivery device is connected to the model and placed in the wind tunnel test section for delivering the model.

[0012] Further, a plurality of feature points are irregularly arranged on the surface of the model to ensure that 6-10 complete feature points are recognized in the fields of view of the first imaging device and the second imaging device.

[0013] Further, the duty cycle of the spots in the random speckle background is 40%-50% and the spots are 3-4 pixels in the image, and the colors of the spots and the blank areas are black and white.

[0014] Further, the first imaging device and the second imaging device are composed of a camera and a lens.

[0015] A method for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves is realized relying on the described device for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves, and includes the following steps:

[0016] S1. Model preparation, arranging feature points irregularly on the surface of the model;

[0017] S2. Conduct three-coordinate surveying and mapping on the centers of the feature points in the model coordinate system to obtain the three-dimensional coordinate sequence of the feature points arranged in step S1 in the model coordinate system;

[0018] S3. Arrange a random speckle background on the inner wall of the test section;

[0019] S4. Build a device for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves, and adjust the camera lens focal length and exposure time of the first imaging device and the second imaging device;

[0020] S5. Obtain the parameters of the first imaging device and the second imaging device. Place a checkerboard calibration board at different positions in the area to be measured, and solve the internal and external parameters of the camera based on the Zhang's calibration method;

[0021] S6. Calibrate the wind tunnel coordinate system. The calibration of the wind tunnel coordinate system is achieved through a checkerboard calibration plate, and the three-dimensional coordinates in the camera coordinate system are converted to those in the wind tunnel coordinate system;

[0022] S7. When there is no wind and no model is installed, the first imaging device and the second imaging device capture random speckle background images to obtain background images with optical noise removed 、 , and then during the wind tunnel test, capture model release images to obtain the image sequences collected by the first imaging device and the image sequences collected by the second imaging device;

[0023] S8. Based on the image sequences collected by the first imaging device and the image sequences collected by the second imaging device obtained in step S7, draw the model release trajectory;

[0024] S9. Solve the shock wave shaping diagram and qualitatively observe the refractive index change in the two-dimensional shock wave phenomenon of the model;

[0025] S10. Visualize the model release trajectory obtained in step S8 and the shock wave shaping diagram obtained in step S9.

[0026] Furthermore, the specific implementation method of step S6 includes the following steps:

[0027] Place the checkerboard calibration plate horizontally on the side wall of the wind tunnel, and ensure that the x-direction of the checkerboard is parallel to the air flow direction, and the y-direction of the checkerboard is parallel to the vertical direction of the ground; By calculating the difference in the three-dimensional coordinates of the corner points in the x-direction of the checkerboard and performing normalization processing, the air flow axis vector is obtained , calculate the difference in the three-dimensional coordinates of the intersection points in the y-direction of the checkerboard and perform normalization processing to obtain the yaw axis vector ; Then based on 、 and the pitch axis vector are both orthogonal, the pitch axis vector is obtained , and finally the axis system transformation matrix from the camera coordinate system to the wind tunnel coordinate system is obtained .

[0028] Furthermore, the specific implementation method of step S7 includes the following steps:

[0029] S7.1. When there is no wind and no model is installed, capture random speckle background images, collect 10 random speckle background images and take the average to eliminate optical noise, and obtain the background images with optical noise removed collected by the first imaging device , the background images with optical noise removed collected by the second imaging device ;

[0030] S7.2. During the wind tunnel test, first reach the test conditions. Then, the computer first sends a trigger signal to the digital pulse signal delay generator to trigger the first imaging device and the second imaging device to perform image acquisition. After a half-second interval, a trigger signal is sent to the delivery device to trigger the model to start being delivered. The first imaging device and the second imaging device collect all the process photos of the model from the start of delivery to the end, obtaining the image sequence collected by the first imaging device and the image sequence collected by the second imaging device , where is the image data at the maximum time collected by the first imaging device is the image data at the maximum time collected by the second imaging device

[0031] Furthermore, the specific implementation method of step S8 includes the following steps

[0032] S8.1. Image preprocessing: Preprocess the images collected in step S7, perform binarization using the Otsu threshold method to generate a black and white image

[0033] S8.2. Contour detection and screening: Extract the contours in the image processed in step S8.1 through the Suzuki contour tracking algorithm, calculate the roundness of the contours to screen out the contours close to a circle. The roundness formula is

[0034] ;

[0035] where is the roundness of the recognized contour is the contour area is the perimeter of the contour. If is less than the given contour threshold , then ignore the recognized contour

[0036] S8.3. Ellipse fitting and affine transformation: Perform ellipse fitting on the screened contours to obtain the center coordinates of the ellipse, the major axis a, the minor axis b, and the rotation angle. Convert the elliptical region into a perfect circle for standardization. The affine transformation matrix M is calculated based on the four vertices of the fitted ellipse and the four vertices of the standard circle

[0037] S8.4. Region transformation and secondary binarization

[0038] According to the center coordinates of the ellipse, and intercept the region according to three times the lengths of the major axis a and the minor axis b to include the local image where the coding points are located ;

[0039] According to the affine transformation matrix M for Perform an affine transformation to obtain the transformed image , and enlarge the image to the standard size to obtain the transformed image of the standard size ; Convert back to a grayscale image and a binary image again;

[0040] S8.5. Encoding point detection:

[0041] Let have the center coordinates of , and set the sampling radius as: the first sampling radius , the second sampling radius , and the third sampling radius is , where is the reference radius, is 1 / 3 of the maximum inscribed circle radius of ;

[0042] At each sampling radius, uniformly select sampling points first. The coordinate calculation formula for the j-th sampling point is:

[0043] ;

[0044] ;

[0045] Among them, j represents the sampling point number index; At each sampling point read the pixel value, and then judge whether it belongs to the encoding point. The judgment rule is whether all sampling points within the radius of are white, whether all sampling points within the radius of are black, and whether the number of sampling points within the radius of > 2; If the above rules are met, it is considered that the extracted area contains the encoding point, and the area containing the encoding point is obtained;

[0046] S8.6. Encoder decoding:

[0047] Extract the encoding information from the area confirmed to contain the encoding point to generate the unique identifier of the encoding point;

[0048] Based on the third sampling radius area, sample N points for extracting encoding information. When sampling, the image is rotated at different angles to eliminate the influence of the angle on the encoding. The coordinate calculation formula for the k-th sampling point used for extracting encoding information is:

[0049] ;

[0050] ;

[0051] Among them, k represents the sampling point index for extracting coding information. is the rotation angle, which is used to change the starting angle of the sampling points circle by circle.

[0052] Perform rotation normalization on the coding results of each circle, then take the mean of the coding results of all circles, perform binarization on the obtained mean coding, obtain the final binary coding, and convert it into a decimal number index as the unique identifier of the coding point.

[0053] S8.7. Draw the model placement trajectory based on the encoder decoding.

[0054] For the image collected by the first imaging device at time t and the image collected by the second imaging device at time t , where , the sequence of central image coordinates of the coding points for identifying the first imaging device is and the sequence of central image coordinates of the coding points for the second imaging device is ;

[0055] Based on and and the internal and external camera parameters obtained in step S6, obtain the three-dimensional coordinate sequence of n coding points in the camera coordinate system, and the coding point sequence corresponding to the n coding points;

[0056] According to the axis transformation matrix from the camera coordinate system to the world coordinate system calculated in step S6, obtain the coordinate sequence of n coding points in the world coordinate system;

[0057] By comparing with the identification sequence corresponding to the coding points on the model in step S2, obtain the coordinate sequence and of the homologous points in the model coordinate system, where is a subset of ; Based on

[0058] and and , calculate the rotation and translation matrix from the model coordinate system to the world coordinate system through the Root Mean Square (RMS) Kabsch algorithm;

[0059] Let the of the center point of the model in the model coordinate system, then the coordinate of this point in the world coordinate system at time t is ;

[0060] For the images captured at any moment, repeating the above steps can obtain the coordinates of the center point of the model in the world coordinate system, and sorting them out to obtain the coordinate sequence on the time axis , and drawing the trajectory according to the time axis to obtain the model delivery trajectory

[0061] Furthermore, the specific implementation method of step S9 includes the following steps:

[0062] S9.1. Automatic extraction of model masks. For any , according to the sequence of encoded point image coordinates on the model calculated by S8, calculate its mean coordinate , and with as the center, with length and width extract the image region , is selected to ensure that completely contains the entire model;

[0063] For , perform binarization processing, use dilation operation to fill the holes in the target area, and combine erosion operation to remove small noise points;

[0064] Use the Canny operator for edge detection to obtain all the contours, and retain the largest contour as the mask of the model by area screening , set the model position value to 0, and the values of other parts to 1;

[0065] S9.2. Calculate the divergence of each pixel in the image;

[0066] The displacement field at each pixel position of the image at time t is expressed as , and has the expression:

[0067] ;

[0068] Among them, is the cross-correlation function, which is used to measure the similarity between two windows. Preferably, the normalized cross-correlation method is selected as the cross-correlation method;

[0069] Based on calculate the divergence of the current pixel;

[0070] S9.3. Establish an adaptive divergence fidelity filter to smooth the noise:

[0071] Introduce weight adjustment for divergence calculation to adaptively smooth high-noise areas. The formula is:

[0072] ;

[0073] Among them, is the weight function, is the divergence after weight adjustment;

[0074] Weight function is defined as:

[0075] ;

[0076] Among them, represents the local vector gradient intensity, is the local variance of the vector field intensity, is a hyperparameter for controlling the weight sensitivity, generally taking values from 0.5 to 2.0;

[0077] ;

[0078] Then, add a smoothing constraint. By minimizing the following expression, the noise is further reduced:

[0079] ;

[0080] Among them, represents the scale factor;

[0081] The optimized divergence obtained is ;

[0082] Repeat the above steps iteratively until convergence to obtain the optimal ;

[0083] S9.4. Process each pixel according to steps S9.1 - S9.3 to obtain the optimized divergence of each pixel, and organize it to obtain the divergence map of the current image , and combine this image with the in step S9.1 to obtain the qualitative shock wave diagram of the elastic model at the final time t , and the expression is:

[0084] .

[0085] Furthermore, the specific implementation method of step S10 is to back-project the in step S8 combined with the internal and external parameters of the camera into to obtain the image coordinates of the center point of the model. Taking the image coordinates of the center point of the model as the center, draw a circle with a radius of 5 pixels, and display the corresponding three-dimensional coordinates above the circle in real time. Superimpose the drawn circle and the image coordinate information of the center point of the model on the displacement divergence image , and for Repeat the above steps for the images captured at any moment, and then display the processed images dynamically to achieve the synchronous visualization of two-dimensional shock waves and the model trajectory.

[0086] Advantages of the present invention:

[0087] A method for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves according to the present invention is based on the basic theories of binocular vision three-dimensional reconstruction and image correlation. By arranging asymmetric coding points on the surface of the object model, the problem of missing marker points caused by the rotation of the object model during the dropping process is solved, ensuring that a sufficient number of marker points can be recognized in any rotation state. This not only improves the robustness of marker point recognition but also realizes the real-time conversion between the wind tunnel coordinate system and the model coordinate system through the corresponding relationship of the coding points in these two coordinate systems. This real-time conversion ability further supports the accurate real-time calculation of the three-dimensional coordinates of any point on the object model in the wind tunnel coordinate system, thereby improving the real-time performance and accuracy of experimental measurements, meeting the requirements of complex dynamic test scenarios, and providing reliable data support for studying the dynamic characteristics and aerodynamic parameters of the object model.

[0088] A method for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves according to the present invention can obtain the two-dimensional shock wave distribution of the object model in real time based on the background schlieren method. Compared with the traditional schlieren method, the present invention does not require complex optical path arrangements, greatly reducing the equipment complexity and installation cost, simplifying the experimental operation process, and significantly improving the experimental efficiency and repeatability.

[0089] A method for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves according to the present invention proposes an adaptive divergence fidelity filter field that can optimize the results of background schlieren, clearly display the boundary of the shock wave, and effectively reduce background noise.

[0090] A method for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves according to the present invention combines trajectory recognition and shock wave visualization, realizing the synchronous measurement and intuitive display of the motion state of the object model and the characteristics of the surrounding flow field, providing strong technical support for in-depth research on the aerodynamic performance, shock wave behavior of the object model, and the interaction between the two. This combined visualization method improves the comprehensive analysis ability of wind tunnel experiments and provides a more comprehensive and efficient solution for aerodynamic research and engineering applications. Description of the drawings

[0091] Figure 1 is a schematic structural diagram of a device for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves according to the present invention;

[0092] Figure 2 is a schematic diagram of irregularly arranging feature points on the model surface according to the present invention;

[0093] Figure 3 This is a flowchart of a method for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves according to the present invention. Specific embodiments

[0094] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in combination with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the specific embodiments described are only a part of the embodiments of the present invention, rather than all of the specific embodiments. The components of the specific embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0095] Therefore, the detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but only represents the selected specific embodiments of the present invention. All other specific embodiments obtained by those skilled in the art based on the specific embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0096] To further understand the content, features and effects of the present invention, the following specific embodiments are exemplified and are accompanied by Figure 1 - Appendix Figure 3 The details are as follows:

[0097] Example 1

[0098] An apparatus for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves includes a first imaging device 4, a second imaging device 5, an L-shaped block 6, a wrench slider 7, a DC high-power LED light source 8, a computer 9, a digital pulse signal delay generator 10, a square guide rail 11, and a random speckle background 12;

[0099] The square guide rail 11 is connected into a double-layer bracket through the wrench slider 7 and is placed outside the side wall plate 13 of the wind tunnel test section corresponding to the optical window 1. The first imaging device 4 and the second imaging device 5 are placed on the upper layer of the double-layer bracket, the DC high-power LED light source 8 is placed in the middle of the lower layer of the double-layer bracket, and the L-shaped blocks 6 are connected to both ends of the lower layer of the double-layer bracket;

[0100] The computer 9 is respectively connected to the first imaging device 4 and the second imaging device 5, and the digital pulse signal delay generator 10 is respectively connected to the first imaging device 4 and the second imaging device 5;

[0101] The random speckle background 12 is arranged on the inner side of the side wall plate 13 of the wind tunnel test section corresponding to the inner wall of the wind tunnel test section;

[0102] The delivery device 3 is connected to the model 2 and placed in the wind tunnel test section for delivering the model 2.

[0103] Furthermore, a plurality of feature points are irregularly arranged on the surface of the model 2 to ensure that 6 - 10 complete feature points can be recognized in the fields of view of the first imaging device 4 and the second imaging device 5.

[0104] Furthermore, the duty ratio of the spots in the random speckle background 12 is 40% - 50% and the spots are 3 - 4 pixels in the image, and the colors of the spots and the blank areas are black and white.

[0105] Furthermore, the first imaging device 4 and the second imaging device 5 are composed of a camera and a lens.

[0106] Embodiment 2

[0107] A method for identifying the trajectory of an object delivered in a wind tunnel and visualizing shock waves is realized relying on the device for identifying the trajectory of an object delivered in a wind tunnel and visualizing shock waves described in Embodiment 1, and includes the following steps:

[0108] S1. Model preparation, arranging feature points irregularly on the model surface;

[0109] Furthermore, the feature points include one or a combination of several of circular coded points, marked points of different colors, and special marks. No matter how the model rolls, at least 6 complete feature points can always be recognized in the field of view.

[0110] S2. Perform three - coordinate mapping on the centers of the feature points in the model coordinate system to obtain the three - dimensional coordinate sequence of the feature points arranged in step S1 in the model coordinate system;

[0111] Furthermore, the specific implementation method of step S2 includes the following steps: Select a high - precision coordinate measuring machine (CMM), and strictly execute the calibration procedure to ensure that the device parameter settings are accurate to meet the requirements of measurement accuracy. Secondly, use a professional fixture or fixing device to firmly install the blade on the measurement platform to ensure that it remains absolutely stationary during the measurement process and avoid any displacement or vibration affecting the measurement results. Then, according to the predetermined measurement strategy, use the measurement probe to sequentially contact the positioning reference points on the blade surface, accurately record the coordinate data of all reference points in three - dimensional space , and then record the identification sequence corresponding to each coded point , where kmax is the total number of feature points.

[0112] S3. Arrange a random speckle background on the inner wall of the test section;

[0113] Further, the specific implementation method of step S3 is to achieve the background through painting and sticking stickers. Preferably, first spray a matte white primer on the inner wall. After the white primer dries, then place the hollowed low-adhesion sticker on the white primer, and then spray a matte black paint. After natural drying, tear off the low-adhesion hollow sticker to achieve the preparation of a random speckle background;

[0114] S4. Set up a device for identifying the trajectory of the object dropped in the wind tunnel and visualizing the shock wave, and adjust the camera lens focal length and exposure time of the first imaging device and the second imaging device;

[0115] Further, the specific implementation method of step S4 includes the following steps:

[0116] Install the first imaging device, the second imaging device, and the DC high-power LED light source outside the optical window on the side wall of the test section through components such as guide rails and sliders, and determine the camera lens focal length and the camera exposure time E according to the field of view and the model movement speed. Among them, the camera focal length can be determined according to the following formula:

[0117] ;

[0118] where FOV represents the field of view, H represents the size of the high-speed camera chip, is the distance from the camera lens to the object being photographed.

[0119] To avoid motion blur, within the exposure time , the drag of the model should not exceed one pixel, that is, the movement length of the model should not exceed 1 pixel, that is is the theoretical maximum speed when the model is dropped;

[0120] S5. Obtain the parameters of the first imaging device and the second imaging device. Place a checkerboard calibration board at different positions within the area to be measured, and solve the internal and external parameters of the camera based on the Zhang's calibration method;

[0121] Further, the internal parameter in step S5 is ; the external parameter is ; where is the focal length in the u and v directions, is the radial distortion parameter, is the image principal point coordinate; R and t are the rotation matrix and translation vector from the world coordinate system to the camera coordinate system respectively.

[0122] S6. Calibrate the wind tunnel coordinate system, achieve the calibration of the wind tunnel coordinate system through the checkerboard calibration board, and convert the three-dimensional coordinates in the camera coordinate system to the wind tunnel coordinate system;

[0123] Further, the specific implementation method of step S6 includes the following steps:

[0124] Horizontally place the checkerboard calibration plate on the side wall of the wind tunnel, and ensure that the x - direction of the checkerboard is parallel to the air flow direction, and the y - direction of the checkerboard is parallel to the vertical direction of the ground; by calculating the difference in the three - dimensional coordinates of the corner points in the x - direction of the checkerboard and performing normalization processing, the air flow axis vector is obtained , calculate the difference in the three - dimensional coordinates of the intersection points in the y - direction of the checkerboard and perform normalization processing to obtain the yaw axis vector ; then based on 、 and the pitch axis vector are all orthogonal, the pitch axis vector is obtained , and finally the axis system transformation matrix from the camera coordinate system to the wind tunnel coordinate system is obtained .

[0125] S7. The first imaging device and the second imaging device take random speckle background images when there is no wind and no model is installed to obtain the background images with optical noise removed 、 , and then take model release images during the wind tunnel test to obtain the image sequences collected by the first imaging device and the image sequences collected by the second imaging device;

[0126] S7.1. When there is no wind and no model is installed, take random speckle background images, collect 10 random speckle background images and take the mean value to eliminate optical noise, and obtain the background images with optical noise removed collected by the first imaging device , the background images with optical noise removed collected by the second imaging device ;

[0127] S7.2. During the wind tunnel test, first reach the test conditions, then the computer sends a trigger signal to the digital pulse signal delay generator to trigger the first imaging device and the second imaging device to perform image acquisition. After a half - second interval, a trigger signal is sent to the release device to trigger the model to start being released. The first imaging device and the second imaging device collect all the process photos of the model from the start of release to the end, and obtain the image sequence collected by the first imaging device 、the image sequence collected by the second imaging device , where is the image data at the maximum time collected by the first imaging device, is the image data at the maximum time collected by the second imaging device.

[0128] S8. Based on the image sequences collected by the first imaging device and the second imaging device obtained in step S7, draw the model release trajectory;

[0129] Further, the specific implementation method of step S8 includes the following steps:

[0130] S8.1. Image preprocessing: Preprocess the image collected in step S7, perform binarization using the Otsu threshold method to generate a black-and-white image;

[0131] S8.2. Contour detection and screening: Extract the contours in the image processed in step S8.1 through the Suzuki contour tracking algorithm, calculate the roundness of the contours to screen out the contours close to a circle; The roundness formula is:

[0132] ;

[0133] where, is the roundness of the recognized contour, is the contour area, is the perimeter of the contour, if is less than the given contour threshold , then ignore the recognized contour;

[0134] S8.3. Ellipse fitting and affine transformation: Perform ellipse fitting on the screened contours to obtain the center coordinates , major axis a, minor axis b and rotation angle of the ellipse, convert the ellipse region into a perfect circle for standardization, and calculate the affine transformation matrix M based on the four vertices of the fitted ellipse and the four vertices of the standard circle;

[0135] S8.4. Region transformation and secondary binarization:

[0136] According to the center coordinates of the ellipse , and intercept the region according to three times the lengths of the major axis a and minor axis b to include the local image where the coding point is located ;

[0137] Perform an affine transformation on according to the affine transformation matrix M to obtain the transformed image , and enlarge this image to the standard size to obtain the transformed image of the standard size ; Convert back to a grayscale image and a binary image again;

[0138] S8.5. Coding point detection:

[0139] Let the center coordinates of be , set the sampling radii as: the first sampling radius , the second sampling radius , the third sampling radius is , where, is the reference radius, is 1 / 3 of the radius of the largest inscribed circle;

[0140] At each sampling radius, uniformly select sampling points first. The coordinate calculation formula for the j-th sampling point is:

[0141] ;

[0142] ;

[0143] where j represents the sampling point number index; At each sampling point read the pixel value, and then judge whether it belongs to the coding point. The judgment rule is whether all sampling points within the radius are all white, whether all sampling points within the radius are all black, and whether the number of sampling points within the radius > 2; If the above rules are met, it is considered that the extracted area contains the coding point, and the area containing the coding point is obtained;

[0144] S8.6. Encoder decoding:

[0145] Extract the coding information from the area confirmed to contain the coding point to generate the unique identifier of this coding point;

[0146] Based on the third sampling radius area, sample N points for extracting coding information. When sampling, the image is rotated at different angles to eliminate the influence of the angle on the coding. The coordinate calculation formula for the k-th sampling point for extracting coding information is:

[0147] ;

[0148] ;

[0149] where k represents the sampling point number index for extracting coding information, is the rotation angle, which is used to change the starting angle of the sampling point circle by circle;

[0150] Perform rotation normalization on the coding results of each circle, and then take the average of the coding results of all circles. The average coding obtained is binarized to obtain the final binary coding, which is converted into a decimal number index as the unique identifier of the coding point;

[0151] Furthermore, perform rotation normalization on the encoding results of each circle to eliminate the influence of the angle on the encoding results. Take the average value of the encoding results of all circles to reduce the influence of noise on the encoding results. The average value at each position represents whether it is "black" or "white" at that position. Perform binarization processing on the average encoding. Set the threshold to 0.5. If the average value at a certain position is greater than 0.5, then that position is "black" (encoded as 1), otherwise it is "white" (encoded as 0). Obtain the final binary encoding. This binary encoding can be converted into a decimal number index as the unique identifier of this encoding point;

[0152] S8.7. Plot the model delivery trajectory based on the encoder-decoder;

[0153] For the image collected by the first imaging device at time t and the image collected by the second imaging device at time t , where , identify the sequence of central image coordinates of the encoding points of the first imaging device as and the sequence of central image coordinates of the encoding points of the second imaging device as ;

[0154] Based on and and the internal and external camera parameters obtained in step S6, obtain the three-dimensional coordinate sequence of n encoding points in the camera coordinate system, and the sequence of encoding points corresponding to n encoding points ;

[0155] According to the axis system transformation matrix from the camera coordinate system to the world coordinate system calculated in step S6, obtain the coordinate sequence of n encoding points in the world coordinate system;

[0156] By comparing with the identification sequence corresponding to the encoding points on the model in step S2, obtain the coordinate sequence and of the homologous points in the model coordinate system , where is a subset of ;

[0157] Based on and , calculate the rotation and translation matrix from the model coordinate system to the world coordinate system through the root mean square Kabsch algorithm;

[0158] Let the of the center point of the model in the model coordinate system, then the coordinates of this point in the world coordinate system at time t are ;

[0159] For the images captured at any moment, repeating the above steps can obtain the coordinates of the center point of the model in the world coordinate system, and sorting them out to obtain the coordinate sequence on the time axis . Drawing the trajectory according to the time axis, the model delivery trajectory can be obtained.

[0160] S9. Solve the shock formation diagram and qualitatively observe the refractive index change in the two-dimensional shock phenomenon of the model;

[0161] Furthermore, the specific implementation method of step S9 includes the following steps:

[0162] S9.1. Automatic extraction of the model mask. For any , according to the encoded point image coordinate sequence on the model calculated by S8, calculate its mean coordinate . Taking as the center, with length and width , extract the image region . The selection of ensures that completely contains the entire model;

[0163] For , perform binarization processing, use dilation operation to fill the holes in the target area, and combine erosion operation to remove small noise points;

[0164] Use the Canny operator for edge detection to obtain all the contours, and retain the largest contour as the mask of the model by area screening , set the model position value to 0 and other parts to 1;

[0165] S9.2. Calculate the divergence of each pixel in the image;

[0166] The displacement field at each pixel position of the image at time t is expressed as , and has the expression:

[0167] ;

[0168] Among them, is the cross-correlation function, used to measure the similarity between two windows. Preferably, the normalized cross-correlation method is selected as the cross-correlation method;

[0169] Based on , calculate the divergence of the current pixel;

[0170] S9.3. Establish an adaptive divergence fidelity filter to smooth the noise:

[0171] By introducing divergence smoothing regularization and a region-related weight function, the robustness of the signal region to noise is enhanced during the divergence calculation, while the boundary details of high-divergence regions (such as shock regions) are retained.

[0172] Introduce weight adjustment to the divergence calculation for adaptively smoothing high-noise regions. The formula is:

[0173] ;

[0174] where is the weight function, is the divergence after weight adjustment;

[0175] The weight function is defined as:

[0176] ;

[0177] where represents the local vector gradient intensity, is the local variance of the vector field intensity, is a hyperparameter that controls the weight sensitivity, generally taking values from 0.5 to 2.0;

[0178] ;

[0179] Then add a smoothing constraint. By minimizing the following expression, the noise is further reduced:

[0180] ;

[0181] where represents the scale factor;

[0182] The square difference of the first term in this formula is to make the optimized divergence as close as possible to the original divergence result, avoiding over-smoothing or deviating from the true physical information, ensuring that the optimization does not destroy the shock details. The second term introduces smoothing regularization, aiming to reduce the drastic fluctuations in the optimized divergence field to suppress high-frequency noise. Make the divergence field smoother and conform to the real physical phenomenon, especially in the background region. The optimized divergence is ;

[0183] Overall, the adaptive divergence fidelity filter is to find an optimal divergence field that has less noise and a smooth visual effect while being as close as possible to the true divergence.

[0184] Repeat the above steps iteratively until convergence to obtain the optimal ;

[0185] S9.4. Process each pixel according to steps S9.1 - S9.3 to obtain the optimized divergence of each pixel, and organize it to obtain the divergence map of the current image. , and combine this image with the in step S9.1 to obtain the qualitative shock wave diagram of the projectile model at the final time t. , and the expression is:

[0186] .

[0187] S10. Perform visualization processing on the model delivery trajectory obtained in step S8 and the shock wave shaping diagram obtained in step S9.

[0188] Furthermore, the specific implementation method of step S10 is to backproject the in step S8 combined with the internal and external parameters of the camera into to obtain the image coordinates of the center point of the model. , draw a circle with a radius of 5 pixels centered on the image coordinates of the center point of the model, and display the corresponding three - dimensional coordinates in real - time above the circle. , superimpose the drawn circle and the image coordinate information of the center point of the model on the displacement divergence image . Repeat the above steps for the images collected at any moment of , and then display the processed images in a dynamic manner to achieve the synchronous visualization of two - dimensional shock wave qualitative analysis and model trajectory.

[0189] It should be noted that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non - exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0190] Although the present application has been described above with reference to specific embodiments, various modifications can be made thereto and components thereof can be replaced with equivalents without departing from the scope of the present application. In particular, as long as there is no structural conflict, the features in the specific embodiments disclosed in the present application can be combined with each other in any way, and the exhaustive description of the combinations is not given in this specification only for the consideration of saving space and resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for identifying the trajectory of an object released in a wind tunnel and visualizing shock waves, which is realized relying on a device for identifying the trajectory of an object released in a wind tunnel and visualizing shock waves, including a first imaging device (4), a second imaging device (5), an L-shaped block (6), a wrench slider (7), a high-power DC LED light source (8), a computer (9), a digital pulse signal delay generator (10), a square guide rail (11), and a random speckle background (12); The square guide rail (11) is connected into a double-layer bracket through the wrench slider (7), and is placed outside the side wall plate (13) of the wind tunnel test section corresponding to the optical window (1). The first imaging device (4) and the second imaging device (5) are placed on the upper layer of the double-layer bracket, the high-power DC LED light source (8) is placed in the middle of the lower layer of the double-layer bracket, and the L-shaped blocks (6) are connected to both ends of the lower layer of the double-layer bracket; The computer (9) is respectively connected to the first imaging device (4) and the second imaging device (5), and the digital pulse signal delay generator (10) is respectively connected to the first imaging device (4) and the second imaging device (5); A random speckle background (12) is arranged on the inner side of the side wall plate (13) of the wind tunnel test section corresponding to the inner wall of the wind tunnel test section; The release device (3) is connected to the model (2) and is placed in the wind tunnel test section for releasing the model (2); It is characterized in that It includes the following steps: S1. Model preparation, arranging feature points irregularly on the model surface; S2. Conduct three-coordinate surveying and mapping on the centers of the feature points in the model coordinate system to obtain the three-dimensional coordinate sequence of the feature points arranged in step S1 in the model coordinate system; S3. Arrange a random speckle background on the inner wall of the test section; S4. Build a device for identifying the trajectory of an object released in a wind tunnel and visualizing shock waves, and adjust the camera lens focal length and exposure time of the first imaging device and the second imaging device; S5. Obtain the parameters of the first imaging device and the second imaging device. Place a checkerboard calibration board at different positions in the area to be measured, and solve the internal and external parameters of the camera based on the Zhang's calibration method; S6. Wind tunnel coordinate system calibration. Realize the wind tunnel coordinate system calibration through the checkerboard calibration board, and convert the three-dimensional coordinates in the camera coordinate system to the wind tunnel coordinate system; S7. The first imaging device and the second imaging device take random speckle background images when there is no wind and the model is not installed to obtain a background image ImgBack without light noise cam1 、ImgBack cam2 , then taking images of the model during the wind tunnel test to obtain an image sequence captured by the first imaging device and an image sequence captured by the second imaging device; S8. Draw the model release trajectory based on the image sequence collected by the first imaging device and the image sequence collected by the second imaging device obtained in step S7; S9. Solve the shock wave shaping diagram and qualitatively observe the refractive index change in the two-dimensional shock wave phenomenon of the model; S10. Perform visualization processing on the model release trajectory obtained in step S8 and the shock wave shaping diagram obtained in step S9.

2. A method for identifying the trajectory of an object released in a wind tunnel and visualizing shock waves according to claim 1, characterized in that: The specific implementation method of step S6 includes the following steps: Place the checkerboard calibration plate horizontally on the side wall of the wind tunnel, and ensure that the x-direction of the checkerboard is parallel to the air flow direction, and the y-direction of the checkerboard is parallel to the vertical direction of the ground; by calculating the difference in the three-dimensional coordinates of the corner points in the x-direction of the checkerboard, the air flow axis vector γ is obtained through normalization processing axis , calculate the difference in the three-dimensional coordinates of the intersection points in the y-direction of the checkerboard, and the yaw axis vector β is obtained through normalization processing axis ; then based on γ axis , β axis and the pitch axis vector are all orthogonal, the pitch axis vector α is obtained axis , and finally the axis system transformation matrix from the camera coordinate system to the wind tunnel coordinate system is obtained 3. A method for trajectory recognition and shock wave visualization of an object released in a wind tunnel according to claim 2, characterized in that: The specific implementation method of step S7 includes the following steps: S7.

1. When there is no wind and no model is installed, capture the random speckle background image, collect 10 random speckle background images and take the mean to eliminate the optical noise, and obtain the background image ImgBack without optical noise collected by the first imaging device cam1 , the background image ImgBack without optical noise collected by the second imaging device cam2 ; S7.

2. During the wind tunnel test, first reach the test conditions. Then the computer first sends a trigger signal to the digital pulse signal delay generator to trigger the first imaging device and the second imaging device to perform image acquisition. After a half-second interval, a trigger signal is sent to the delivery device to trigger the start of the model delivery. The first imaging device and the second imaging device collect all the process photos of the model from the start of delivery to the end, and obtain the image sequence collected by the first imaging device The image sequence collected by the second imaging device Among them, is the image data at the maximum time collected by the first imaging device, is the image data at the maximum time collected by the second imaging device.

4. A method for identifying the trajectory of an object released in a wind tunnel and visualizing shock waves according to claim 3, characterized in that: The specific implementation method of step S8 includes the following steps: S8.

1. Image preprocessing: Preprocess the images collected in step S7, perform binary processing using the Otsu threshold method to generate black and white images; S8.

2. Contour detection and screening: Extract the contours in the images processed in step S8.1 through the Suzuki contour tracking algorithm, and calculate the roundness of the contours to screen out the contours close to circular; The roundness formula is: Among them, R0 is the roundness of the recognized contour, area is the contour area, length is the contour perimeter. If R0 is less than the given contour threshold R limit , then the recognized contour is ignored; S8.

3. Ellipse Fitting and Affine Transformation: Perform ellipse fitting on the selected contours to obtain the center coordinates (c x , c y ), major axis a, minor axis b, and rotation angle of the ellipse, and convert the ellipse region into a perfect circle for standardization. The affine transformation matrix M is calculated based on the four vertices of the fitted ellipse and the four vertices of the standard circle; S8.

4. Region transformation and secondary binary processing: According to the center coordinates (c x , c y ) of the ellipse, and intercept the area by three times the lengths of the major axis a and the minor axis b to include the local image Img RoI where the coding point is located; Perform an affine transformation on Img according to the affine transformation matrix M RoI to obtain the transformed image Enlarge the image to the standard size to obtain the transformed image of the standard size Convert back to grayscale and binary images again; S8.

5. Encoding Point Detection: Let have the center coordinates (X c , Y c ), and set the sampling radii as follows: the first sampling radius R1 = 0.8·R ref , the second sampling radius R2 = 1.6·R ref , and the third sampling radius R3 = 2.4·R ref , where R ref is the reference radius, and R ref is one-third of the maximum inscribed circle radius of At each sampling radius, uniformly select N s sampling points first. The coordinate calculation formula for the j-th sampling point is as follows: Among them, j represents the sampling point index; at each sampling point (x j , y j ), the pixel value is read, and then it is judged whether belongs to the coding point. The judgment rule is whether all sampling points within the radius of R1 are white, whether all sampling points within the radius of R2 are black, and whether the number of sampling points within the radius of R3 is > 2; if the above rules are met, it is considered that the extracted area contains the coding point, and the area containing the coding point is obtained. S8.

6. Encoder Decoding: Extract the encoding information from the area confirmed to contain the encoding point to generate a unique identifier for the encoding point; Sample N points based on the third sampling radius area for extracting encoding information. When sampling, the image is rotated at different angles to eliminate the influence of the angle on the encoding. The coordinate calculation formula for the k-th sampling point used for extracting encoding information is: where k represents the index of the sampling points used for extracting encoding information, and θ is the rotation angle, which is used to gradually change the starting angle of the sampling points in each circle; Perform rotation normalization on the encoding results of each circle, and then take the mean of the encoding results of all circles. The obtained mean encoding is binarized to obtain the final binary encoding, which is converted into a decimal number index as the unique identifier of the encoding point; S8.

7. Draw the model delivery trajectory based on encoder decoding; The image acquired by the first imaging device at time t The image acquired by the second imaging device at time t where t ∈ (0, tmax), the sequence of image coordinates of the coding point centers for identifying the first imaging device is The sequence of image coordinates of the coding point centers for the second imaging device is Based on ptsc cam1 and ptsc cam2 And based on the internal and external camera parameters obtained in step S6, obtain the three-dimensional coordinate sequence pts3d of n coded points in the camera coordinate system camera ={pts1,…,pts n}, and the coded point sequence corresponding to the n coded points According to the axis system transformation matrix R from the camera coordinate system to the world coordinate system calculated in step S6 axis , the coordinate sequence of n coded points in the world coordinate system is obtained By comparing Idx cam with the identification sequence Idx corresponding to the coding points on the model in step S2 base , the coordinate sequences of the homologous points of pts3d camera , pts3d world in the model coordinate system are obtained wherein is a subset of; Based on pts3d world and Calculate the rotation and translation matrix [R t |T t from the model coordinate system to the world coordinate system through the root mean square Kabsch algorithm; Let the center point of the model in the model coordinate system be Then the coordinates of this point in the world coordinate system at time t are Repeat the above steps for the images collected at any moment \(t\in(0, t_{max})\), and the coordinates of the center point of the model in the world coordinate system can be obtained. After sorting, a coordinate sequence on the time axis can be obtained. Draw the trajectory according to the time axis to obtain the model delivery trajectory.

5. A method for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves according to claim 4, characterized in that: The specific implementation method of step S9 includes the following steps: S9.

1. Automatic extraction of model masks. For any sequence of encoded point image coordinates on the model calculated according to S8 calculate its mean coordinate Taking as the center, with length H m and width W m extract the image region H m and W m are selected to ensure that completely contains the entire model; Pair Perform binarization, use dilation operation to fill the holes in the target area, and combine erosion operation to remove small noise points; Use the Canny operator for edge detection to obtain all contours, and retain the largest contour as the mask Mask of the model by means of area screening t , set the model position value to 0 and the values of other parts to 1; S9.

2. Calculate the divergence of each pixel in the image; The displacement field at each pixel position of the image at time t is expressed as u(x, y), v(x, y), and has the expression: where C is the cross-correlation function used to measure the similarity between two windows, and the normalized cross-correlation method is selected as the cross-correlation method; Calculate the divergence of the current pixel based on u(x, y) and v(x, y) S9.

3. Establish an adaptive divergence fidelity filter to smooth the noise: Introduce weight adjustment for divergence calculation to adaptively smooth high-noise regions. The formula is: div′(F) = W(x, y)·div(F) where W(x, y) is the weight function, and div'(F) is the divergence after weight adjustment; The weight function W(x, y) is defined as: Among them, represents the local vector gradient intensity, and σ 2 is the local variance of the vector field intensity, and γ is a hyperparameter for controlling the weight sensitivity, generally taking 0.5 - 2.0; Then add a smoothing constraint to further reduce the noise by minimizing the following expression: where λ represents the scale factor; The optimized divergence is div ’‘ (F) = argmin div ’ε; Repeat the above steps iteratively until convergence to obtain the optimal div″(F); S9.

4. Process each pixel according to steps S9.1 - S9.3 to obtain the optimized divergence of each pixel, and organize to obtain the divergence map of the current image. Combine this image with the Mask in step S9.1 t Obtain the qualitative shock wave diagram of the projectile model at the final moment t The expression is:

6. A method for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves according to claim 5, characterized in that: The specific implementation method of step S10 is to back-project the combined with the internal and external parameters of the camera into to obtain the image coordinates (u c , v c ) of the center point of the model. Taking the image coordinates of the center point of the model as the center, draw a circle with a radius of 5 pixels, and display the corresponding three-dimensional coordinates above the circle in real time. Superimpose the drawn circle and the image coordinate information of the center point of the model on the displacement divergence image . Repeat the above steps for the images collected at any moment when t ∈ (0, tmax), and then display the processed images in a dynamic manner to achieve the synchronous visualization of two-dimensional shock wave qualitative analysis and model trajectory.

7. A method for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves according to claim 6, characterized in that, A plurality of feature points are irregularly arranged on the surface of the model (2) to ensure that 6 - 10 complete feature points are recognized in the fields of view of the first imaging device (4) and the second imaging device (5).

8. A method for identifying the trajectory of an object released in a wind tunnel and visualizing shock waves according to claim 7, characterized in that, The duty cycle of the spots in the random speckle background (12) is 40% - 50% and the spots are 3 - 4 pixels in the image. The colors of the spots and the blank areas are black and white.

9. A method for identifying the trajectory of an object dropped in a wind tunnel and visualizing shock waves according to claim 8, characterized in that The first imaging device (4) and the second imaging device (5) are composed of a camera and a lens.

Images