CN114323543A - Method for improving pressure-sensitive paint test efficiency - Google Patents
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Abstract
The invention discloses a method for improving the test efficiency of pressure-sensitive paint, which comprises the following steps: collecting a dark image and a reference image of a model surface PSP coating at each attack angle before wind blowing of a wind tunnel is started; switching the working state of exciting light to be on after the wind tunnel is started, and acquiring a test image of the model surface PSP coating at each attack angle based on a camera; thirdly, obtaining a ratio image based on the marking and processing of the dark image, the reference image and the test image; step four, based on a calibration relational expression of the light intensity ratio and the pressure of the pressure-sensitive paint obtained before the test, converting the ratio image to obtain a pressure image of the surface of the model; and step five, converting the pressure image based on the known wind tunnel inflow parameters to obtain a corresponding pressure coefficient image. The invention provides a method for improving the test efficiency of pressure-sensitive paint, which is used for reducing the blowing time of the existing PSP technology, further improving the test efficiency and saving the test cost.
Description
Technical Field
The invention relates to the technical field of wind tunnel tests. More specifically, the invention relates to a method for improving the test efficiency of pressure-sensitive paint in the optical pressure-sensitive paint wind tunnel test technology.
Background
In wind tunnel tests, surface pressure measurement is one of the most basic means for knowing the aerodynamic performance of an aircraft, and is an indispensable test technology for overall and structural design, aerodynamic load distribution prediction and strength check of the aircraft. Pressure measurements provide key information for many important flow phenomena, such as shock waves, flow separation, vortex development, etc. In addition, accurate pressure measurement data also plays a key role in validating and validating numerical simulation methods. The traditional pressure measurement method mainly adopts a mode of distributing pressure measuring holes on the surface of a model and measuring the object surface pressure by connecting a pressure sensor or an electronic scanning valve through a pipeline, and has the defects of high measurement precision, low spatial resolution, complex model processing, complex test preparation and other engineering use limitations. Since Kautsky H and Hirsch H, German chemists in the 30 th century, discovered for the first time the "oxygen quenching" effect of oxygen molecules in rapidly extinguishing their luminous intensity during the fluorescence or phosphorescence process of certain chemicals, they attempted to be utilized in practice. Since the 80 s in the 20 th century, mankind creatively invented a Pressure Sensitive Paint Pressure measurement technology (Pressure Sensitive Paint-PSP) by utilizing photoluminescence of high molecular organic matters and quenching effect of oxygen molecules on photoluminescence intensity and combining achievements of optics, information technology and graphic image processing technology, and widely applied to the fields of aerospace, automobile industry, high-rise building stability research and the like.
The PSP technology is a measuring method which utilizes photoluminescence characteristics of high molecular organic matters (probe molecules) and oxygen quenching effect of deactivating the excited probe molecules by oxygen molecules, irradiates a tested object surface fully coated with probe molecule-containing pressure-sensitive paint with an excitation light source with proper wavelength, captures a gray level image of the surface of a coating by a light intensity collecting device, and obtains a pressure distribution map of the tested object surface through image processing and gray level and pressure conversion. The technology measures the continuous change pressure map of the whole area of the model surface in a non-contact mode, can more intuitively, comprehensively and accurately reflect the pressure structure condition of the tested object surface in the air flow, makes up and avoids the inherent defects and the defects of the traditional pressure measurement method, and embodies the irreplaceable unique advantages. In recent ten years, pressure sensitive paint pressure measurement technology, especially pressure sensitive paint pressure measurement technology based on a light intensity method, has been rapidly developed in various scientific research institutions and colleges at home and abroad, and has entered the engineering application stage at present, so that the pressure sensitive paint pressure measurement technology becomes a necessary test means for various production type wind tunnels at home and abroad.
The PSP test technology has the advantages of non-contact flow field no disturbance, high spatial resolution and the like, but a plurality of test error sources exist, and collected image noise is one of important error sources and must be suppressed to ensure high-precision measurement of the PSP technology. Due to the influences of probe molecule condensation, CCD camera noise and the like in the PSP coating, the PSP image acquired in the test has large-range salt and pepper noise, and the light intensity of the image is in fluctuation distribution. Taking a model surface PSP pressure measurement test developed by a 2.4-meter-magnitude wind tunnel as an example, in order to weaken the influence of salt-pepper noise of an image, 10 images are generally collected for average processing, meanwhile, in order to ensure higher image signal-to-noise ratio, the exposure time of a camera is about 1 second, the total blowing time of a test state reaches about 120 seconds, the blowing time of a traditional pressure measurement method taking a pressure sensor as a measuring device in a test state is only about 50 seconds, the blowing time of the PSP test method is far longer than that of the traditional test method, the test efficiency is lower, and the test energy consumption and the test cost are also greatly increased compared with those of the traditional method.
In a word, the surface pressure measurement method using the pressure sensor as a measurement device has engineering use limitations of low spatial resolution, complex model processing, complex test preparation and the like, and the PSP technology has the advantages of no disturbance of a non-contact flow field, high spatial resolution and the like, can more intuitively, comprehensively and accurately reflect the pressure structure condition of a measured object surface in air flow, makes up and avoids inherent defects and shortcomings of the traditional pressure measurement method, but has the defects of long air blowing time, low test efficiency, large test energy consumption and high test expenditure. In order to improve the engineering practicability and the economical efficiency of the PSP technology, the blowing time of the technology must be reduced, the test efficiency is improved, and the test cost is saved.
Disclosure of Invention
An object of the present invention is to solve at least the above problems and/or disadvantages and to provide at least the advantages described hereinafter.
To achieve these objects and other advantages in accordance with the purpose of the invention, there is provided a method for improving the efficiency of a pressure sensitive paint test, comprising:
before wind blowing of a wind tunnel is started, dark images and reference images of a model surface PSP coating at each attack angle are collected based on a camera by switching the working state of exciting light;
switching the working state of exciting light to be on after the wind tunnel is started, and acquiring a test image of the model surface PSP coating at each attack angle based on a camera;
thirdly, obtaining a corresponding ratio image based on the marking and Gaussian filtering processing of the dark image, the reference image and the test image;
step four, converting the ratio image to obtain a pressure image of the surface of the model based on a calibration relational expression of the light intensity ratio and the pressure of the pressure-sensitive paint obtained after the test;
converting the pressure image based on the known wind tunnel inflow parameters to obtain a corresponding pressure coefficient image;
in the first step, when acquiring a dark image and a reference image, respectively acquiring 10 initial dark images and 10 initial reference images at each attack angle through a camera, and respectively performing average processing on the 10 initial dark images and the 10 initial reference images to obtain a corresponding dark image and a corresponding reference image;
in step two, the test image is configured to collect only one corresponding test image at each angle of attack;
in step three, the gaussian filter diameter is configured to be 30 pixels, and the number of iterations is configured to be 15.
Preferably, in step three, the marking and processing are configured to include:
s30, loading the dark image, the reference image and the test image, and performing mark point selection, mark point identification and mark point positioning on the reference image and the test image to obtain a mark point coordinate file for storing and positioning;
s31, registering the test image to the position of the reference image based on the coordinate relation of the mark points, checking the registration precision, if the precision meets the preset requirement, storing the registered test image, and entering the step S32, if the precision does not meet the standard, returning to the step S30;
s32, respectively subtracting the background image from the reference image and the test image, and filling the image in the area without the photosensitive paint in the image to obtain a filled reference image I and a filled test image I;
s33, setting the region outside the model as a background region, wherein the light intensity of the reference image I and the test image I in the background region is not assigned, and performing Gaussian filtering on the model region image outside the background region to obtain a filtered reference image II and a filtered test image II;
and S34, carrying out ratio processing on the second reference image and the second test image to obtain a corresponding ratio image.
Preferably, in step S30, the marker points are black circles uniformly distributed around the model image and have a number of at least 12, and the marker point locations are configured to use a centroid method, a centroid method and a template-based cross-correlation method.
Preferably, in S31, the registration is configured to adopt a registration manner based on the feature information to fit the correlation between the reference image and the test image by extracting and positioning the feature points in the reference image and the test image, so as to obtain the registration relationship between the test image and the reference image;
mapping the gray values of pixel points of the test image one by one to a reference image based on the registration relation;
and (4) the mapped test image is not on the integer pixel point, the gray value on the integer pixel point is calculated by using an interpolation method, and the registration of the test image is finally completed.
Preferably, in S32, the padding is configured to include:
s320, selecting a processing area which is to contain and exceed an area needing to be filled with the image;
s321, filling the processing area image by adopting an interpolation method, wherein an interpolation function in the interpolation method is an algebraic polynomial.
Preferably, in S33, the setting of the background region is configured to be obtained by a thresholding method;
the filter function form of the Gaussian filtering method is configured as follows:
wherein,
is the coordinate of the pixel point, and the coordinate of the pixel point,
is configured to be 1.5.
Preferably, in the fourth step, the light intensity ratio of the pressure-sensitive paint after test and the pressure calibration relation are in the form of:
wherein, P is the pressure,
in order to calibrate the coefficients for the calibration,
the ratio of the light intensity of the pressure-sensitive image to the light intensity of the reference image in the two states of blowing and not blowing is shown, and T is temperature.
Preferably, the manner of obtaining the pressure-sensitive paint light intensity ratio and pressure calibration relation is configured to include:
s40, before the wind tunnel test, based on the parameter setting of the calibration box and the relevant software, obtaining a pre-test calibration fitting relational expression and a fitting curved surface of the pressure-sensitive paint coating under the conditions that the atmospheric pressure and the temperature at the calibration moment are used as reference pressure and temperature;
and S41, during the wind tunnel test, inversely calculating the coating light intensity ratio under the real reference condition based on the pre-test calibration fitting relational expression by actually measuring the atmospheric pressure and the ambient temperature in the tunnel, and obtaining the post-test calibration fitting relational expression and the fitting curved surface of the pressure-sensitive paint coating under the condition that the atmospheric pressure and the temperature in the tunnel during the wind tunnel test are taken as the reference conditions.
The invention at least comprises the following beneficial effects: firstly, only 1 test image is needed to be collected in the test process, compared with the conventional PSP method, the test blowing time is shortened by 2/3, the test expenditure is saved by 66%, and the test efficiency and the economy are greatly improved.
Secondly, the method effectively reduces the salt and pepper noise of the image and greatly improves the pressure post-processing precision of a single test image through large-range and multiple times of Gaussian filtering, and the overall measurement precision of the method is equivalent to that of the conventional multi-image averaging method.
Thirdly, aiming at a single test image, the method optimizes the post-processing flow of the image in the existing PSP technology, effectively avoids the influence of the background area on the image edge caused by the existing flow, and improves the edge fidelity of the pressure result image. The method effectively improves the test efficiency and greatly reduces the test cost on the premise of not reducing the measurement accuracy, and has higher popularization and application values.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
FIG. 1 is a pressure coefficient map of a single test image model obtained by conventional PSP filtering;
FIG. 2 is a pressure coefficient map of a single test image model obtained by filtering according to the present invention;
FIG. 3 is a pressure coefficient error contrast plot obtained for two filtering methods;
FIG. 4 is a pressure coefficient map obtained by a conventional PSP image post-processing procedure;
FIG. 5 is a pressure coefficient map obtained by the image post-processing flow of the present invention;
FIG. 6 is a process flow diagram of the present invention;
FIG. 7 is a map of a pressure system obtained by averaging 10 conventional images;
FIG. 8 is a map of a pressure system obtained using 1 image of the present invention;
fig. 9 is a typical cross-sectional pressure coefficient measurement accuracy contrast plot obtained in example 1 by using the method provided by the present invention and the conventional PSP method.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
The technical problem is solved by the following technical scheme: a method for improving the test efficiency of a pressure-sensitive paint technology comprises the following steps:
and S1, before the wind tunnel blowing is started, the exciting light is turned on, and the camera acquires the light image emitted by the PSP coating on the model surface at each attack angle, namely the reference image. After the collection is finished, the exciting light is turned off, and the camera collects dark images at each attack angle;
s2, starting the wind tunnel, turning on exciting light after a flow field is stable, and collecting a light image emitted by a PSP coating on the surface of the model, namely a test image, by a camera at each attack angle;
s3, loading the dark image, the reference image and the test image collected by the camera, selecting the mark points, identifying the mark points and positioning the mark points on the reference image and the test image, and storing the positioned coordinate file of the mark points;
s4, registering the test image to the position of the reference image according to the coordinate relation of the mark points, checking the registration precision, if the precision reaches the standard, storing the registered test image, entering the step S5, and if the precision does not reach the standard, returning to the step S3;
s5, subtracting the background image from the reference image, subtracting the background image from the test image, and filling images in areas without sensitive paint, such as screw holes, pressure taps and the like in the images to obtain the filled reference image and the filled test image;
s6, setting the region outside the model as a background region, and not assigning the light intensity of the reference image and the test image in the background region so as to reduce the subsequent image processing time and improve the image processing efficiency, and filtering the image of the model region outside the background region to obtain the filtered reference image and the filtered test image;
and S7, carrying out ratio processing on the reference image and the test image to obtain a ratio image, converting the ratio image to obtain a model surface pressure image according to a calibration relational expression of the pressure-sensitive paint light intensity ratio and the pressure obtained before the test, and further converting the pressure image to obtain a final pressure coefficient image according to the known wind tunnel inflow parameters.
Further, the step S1 is specifically:
s11, considering that the dark image and the reference image are acquired before the wind tunnel is blown to start, the blowing time and the test energy consumption are not increased, 10 images can be acquired according to the current conventional PSP technology test flow standard, and the 10 images are averaged to obtain a dark image and a reference image.
Further, the step S2 is specifically:
s21, in order to reduce salt and pepper noise of the images, at least 10 test images are required to be acquired and subjected to average processing according to the standard of the conventional PSP technical test process, and considering that the test images are acquired after wind tunnel blowing is started, the increase of the acquired number of the test images can cause the increase of the blowing time, so that the test efficiency is reduced, and the test expenditure is increased. In the step, only 1 test image is collected, so that the test efficiency is greatly improved, and the test cost is reduced.
Further, the step S3 is specifically:
and S31, marking points are black circles and are uniformly distributed around the model image, and the number of the marking points is at least 12. The marking point positioning adopts a centroid method, a centroid method and a template-based cross-correlation method.
Further, in step S31, the centroid method is a positioning method for finding the gray centroid of the mark point, and the centroid position is obtained by the following formula:
wherein,
is the coordinate of the pixel point, I is the gray value of the pixel point,
is the mark point centroid.
Further, in step S31, the centroid method first extracts edges of the image according to the gray gradient information of the image, and finds the centroid through setting a series of criteria (length criteria, roundness criteria, concavity and convexity criteria, and distance criteria), thereby completing the positioning of the mark point.
Further, in step S31, the template-based cross-correlation method is to use a template image to perform a moving search in the mark point image, but when the cross-correlation similarity measure between the gray values of the mark point image occupied by the template image and the gray values of the mark point image occupied by the template image is extremely large, the mark point position can be determined, and the cross-correlation similarity measure is as follows:
wherein,
is a measure of the cross-correlation of similarity,
is the gray scale of the template image,
and the gray scale of the image of the mark point. Aiming at smaller mark points in the image, in order to enhance the reliability of mark point positioning, the template gray scale is expanded into a partial differential form, and the specific expansion method is as follows:
wherein,
representing the gray value of the mark point,
and
the partial differentials of the grey values in the x and y directions, respectively.
Further, the step S4 is specifically:
s41, obtaining a registration relation between the test image and the reference image by extracting and positioning the characteristic points in the reference image and the test image and fitting the correlation between the reference image and the test image by adopting a registration method based on characteristic information, namely a point mapping method and a Delaunay triangle method;
s42, mapping the gray values of pixel points of the test image one by one to a reference image by using a registration relation;
and S43, the mapped test image is not on the integer pixel point, the gray value on the integer pixel point is calculated by using an interpolation method, and the registration of the test image is finally completed.
Further, in step S41, the point mapping method is a method of performing image registration by using the correlation between the corresponding mark points of the two images, and the key is to find the transformation relationship between the two images, which is generally classified into rigid transformation, affine transformation, projective transformation, and nonlinear transformation.
Further, in step S41, the rigid transformation of the point mapping method refers to a transformation method in which the distance between two points in one graph remains unchanged after transforming into another graph, and the rigid transformation can be decomposed into translation, rotation, and mirror image. The rigid transformation relation is:
wherein,
in order to test the coordinates of the characteristic points of the sequence images,
in order to refer to the coordinates of the feature points of the image,
is the angle of rotation,
is a translation vector.
Further, in step S41, the affine transformation of the point mapping method refers to the transformation in which the straight line on the first image is mapped to the straight line on the second image and the parallel relationship is maintained, and the rigid transformation can be decomposed into a linear transformation and a translational transformation. The affine transformation relation is:
wherein,
in order to test the coordinates of the characteristic points of the sequence images,
in order to refer to the coordinates of the feature points of the image,
in the form of a matrix of real numbers,
is a translation vector.
Further, in step S41, the projective transformation of the point mapping method refers to the transformation that the straight line on the first image is mapped to the straight line on the second image after transformation, but the parallel relationship is not maintained, and the projective transformation relation is:
wherein,
in order to test the coordinates of the characteristic points of the sequence images,
in order to refer to the coordinates of the feature points of the image,
is a real number matrix.
Further, in step S41, the non-linear transformation of the point mapping method may implement any transformation of the two images in principle, and the non-linear transformation relation is:
wherein,
in order to test the coordinates of the characteristic points of the sequence images,
f is any functional form for reference image feature point coordinates.
Further, in step S41, the functional form of the nonlinear transformation of the point mapping method is a polynomial transformation, and the polynomial functional relation is:
wherein,
in order to test the coordinates of the characteristic points of the sequence images,
in order to refer to the coordinates of the feature points of the image,
is a polynomial coefficient.
Further, in step S41, the delaunay triangulation method divides the test image and the reference image into delaunay triangulation networks, vertices of the triangulation networks are marked points, and any pixel point in the triangulation network can be represented by a vector coordinate
There is described a method of, wherein,
is the vector value of the vertex of the triangle,
are parametric coordinates. When the parametric coordinates of a pixel in the triangle in the test image are known, the position vector of the pixel point mapped to the reference image can be determined by
And obtaining the gray value of the pixel point in the test image, and finally mapping the gray value of the pixel point in the test image to the corresponding position of the reference image to finish the registration of the test image. The parametric coordinates are found by:
wherein,
is the coordinate of the pixel point in the triangle,
is the triangle vertex coordinate.
Further, the step S5 is specifically:
s51, selecting a processing area which is to contain and exceed the area to be filled with the image;
s52, filling the processing region image by adopting an interpolation method, wherein the interpolation function is an algebraic polynomial, and the interpolation method comprises Lagrange interpolation, Newton interpolation, piecewise linear interpolation and Hermite interpolation;
further, in step S52, the lagrangian interpolation method takes the following form:
wherein,
in order to be a function of the polynomial interpolation,
in order to interpolate the basis functions of the image,
the gray value of the known pixel point, x is the coordinate of the known pixel point, and i is the rank number of the known pixel point. The interpolation basis function is of the form:
further, in step S52, the newton interpolation method takes the following form:
wherein,
in order to be a function of the polynomial interpolation,
is the gray value of the known pixel point, x is the coordinate of the known pixel point, n is the number of the known pixel points,
is composed of
The specific form of the mean square deviation of (1) is as follows:
further, in step S52, the piecewise linear interpolation method takes the following form:
wherein,
and (4) taking a polynomial interpolation function, wherein x is the coordinate of a known pixel point, n is the number of the known pixel points, i is the sequence number of the known pixel points, and y is the gray value of the known pixel points.
Further, in step S52, the hermitian interpolation method takes the form of:
wherein,
and the function is a polynomial interpolation function, wherein x is the coordinate of a known pixel point, k is the sequence number of the known pixel point, and y is the gray value of the known pixel point.
Further, the step S6 is specifically:
s61, setting a background area by adopting a threshold value method;
and S62, filtering the image by adopting a Gaussian filtering method.
Further, in step S61, the threshold value method is divided into an absolute threshold value method and a relative threshold value method, wherein the absolute threshold value method adopts a method that the pixel light intensity is less than the set threshold value as the background, and the relative threshold value method adopts a method that the ratio of the pixel light intensity to the maximum light intensity of the whole map is less than the set threshold value as the background.
Further, in step S62, the filter function of the gaussian filtering method is in the form of:
wherein,
is the coordinate of the pixel point, and the coordinate of the pixel point,
the size of (d) determines the width of the gaussian function, taken to be 1.5.
Further, in step S62, the filtering diameter of the gaussian filtering method is 30 pixels, and the number of iterations is 15. The PSP technology at present usually adopts a small-range and few-iteration filtering setting, the diameter of a Gaussian filter is generally 5 pixels, and the iteration times are 3 times. However, in order to reduce the blowing time, the method only acquires 1 test image, and cannot reduce the image noise by a multi-image averaging method in the conventional PSP test. Through the large-range and multiple times of Gaussian filtering in the step S62, salt and pepper noise of the image can be effectively reduced through a large number of test tests, and the pressure processing precision of a single test image is greatly improved. Fig. 1 and fig. 2 show a pressure coefficient comparison map of a single test image model obtained by a conventional PSP filtering method and a filtering method adopted in the present invention, and it can be seen that the noise level of the pressure coefficient result map can be effectively reduced by the filtering method of the present invention. Fig. 3 shows a pressure coefficient error comparison point diagram obtained by two filtering methods, and it can be seen that, when the filtering method of the present invention is used for single test image post-processing, the pressure coefficient error is less than 0.02, while the maximum error of the conventional PSP filtering method is as high as 0.05, the filtering method of the present invention can effectively improve the single image post-processing precision.
Further, the step S6 optimizes the post-processing flow of the existing PSP technology image, and sets the background region first, and then performs image filtering, so as to effectively avoid the influence of the background region on the edge of the model light intensity image caused by the background setting after filtering in the existing flow, and avoid the distortion and the deletion of the model light intensity edge image, for the case that only 1 test image is acquired. The model surface pressure coefficient contrast maps obtained by the existing PSP process and optimization process are shown in fig. 4 and 5.
Further, in step four, the obtaining manner of the pressure-sensitive paint calibration coefficient relation is configured to include:
s40, before a wind tunnel test, based on parameter setting of a calibration box and related software, obtaining a pre-test calibration fitting relational expression and a fitting curved surface of the pressure-sensitive paint coating under the conditions that atmospheric pressure and temperature at the calibration moment are used as reference pressure and temperature, wherein the pre-test fitting relational expression is expressed as follows:
wherein, P is the pressure under each station,
the ratio of the light intensity of each station image to the light intensity of the reference image, T is the temperature under each station,
a calibration coefficient is to be calculated;
and S41, during the wind tunnel test, inversely calculating the coating light intensity ratio under the real reference condition based on the pre-test calibration fitting relational expression by actually measuring the atmospheric pressure and the ambient temperature in the tunnel, and obtaining the post-test calibration fitting relational expression and the fitting curved surface of the pressure-sensitive paint coating under the condition that the atmospheric pressure and the temperature in the tunnel during the wind tunnel test are taken as the reference conditions. Specifically, during a wind tunnel test, real-time atmospheric pressure P 'and ambient temperature T' in a wind tunnel test section are measured through a pressure sensor and a temperature sensor which are arranged in a wind tunnel, and P 'and T' are substituted into a pre-test calibration fitting relational expression to obtain the ratio of the coating light intensity under atmospheric pressure and temperature during the test to the coating light intensity under atmospheric pressure and temperature during the pre-test calibration, and further the coating light intensity under atmospheric pressure and temperature during the test is calculated;
the coating light intensity under atmospheric pressure and temperature during the test is taken as the reference image light intensity, the light intensity values under different stations obtained during the calibration before the test are compared with the reference image light intensity again, the light intensity ratio sequence of each station under the reference condition of the atmospheric pressure and the temperature under the actual wind tunnel test state is obtained, the pressure value sequence, the light intensity ratio sequence and the temperature sequence set by each station are fitted again, the calibration fitting relational expression and the fitting curved surface after the test of the pressure-sensitive paint coating are obtained, the fitting relational expression is adopted as a polynomial expression, and the expression can be expressed as follows:
wherein, P is the pressure,
in order to calibrate the coefficients for the calibration,
the ratio of the light intensity of the pressure-sensitive image to the light intensity of the reference image in the two states of blowing and not blowing is shown, and T is temperature. By adopting the scheme, the traditional calibration method is optimized, and the tested calibration formula is substituted into the system, namely the atmospheric pressure and the ambient temperature in the tunnel are actually measured during the wind tunnel test during calibration, andthe coating light intensity value under the real reference condition is inversely calculated by using the pressure-sensitive paint calibration relational expression obtained before the test, so that the accurate pressure-sensitive paint calibration relation taking the atmospheric pressure and the temperature in the tunnel as the reference conditions during the wind tunnel test is obtained, the calibration error is reduced, and the pressure measurement accuracy of the pressure-sensitive paint technology is improved. Therefore, aiming at a certain pressure-sensitive paint and measuring equipment, the method can meet the use requirements of the pressure-sensitive paint wind tunnel test in different regions and seasons only by carrying out paint calibration once. The invention optimizes the existing pressure-sensitive paint calibration method, simplifies the pressure-sensitive paint calibration test and the preparation process before the wind tunnel test, improves the applicability and the calibration precision of the pressure-sensitive paint calibration result, and has higher popularization and application values.
Example 1
The test model of this embodiment is an all-metal train head model, and on the model surface, from bottom to top covers pressure sensitive paint priming paint and pressure sensitive paint finish in proper order. The measuring device comprises a pressure-sensitive paint, a camera, an excitation light source, a synchronous trigger and a data processing industrial personal computer. The pressure-sensitive paint consists of a primer and a finish, wherein the primer is a substrate emitting layer and is a white primer containing silicon dioxide, and the white primer is sprayed on the surface of a model and plays the roles of improving the surface adhesion of the model, enhancing the luminous intensity of probe molecules and isolating heat. The finishing paint is called a polymer functional layer and contains pressure-sensitive probe molecules, wherein the probe molecules are platinum polymers and are main luminescent materials of the pressure-sensitive paint. The camera is a scientific grade CCD camera, the gray dynamic range is 14 bits, the spatial resolution is 4032 multiplied by 2688 pixels, the pixel size is 9 mu m, the refrigeration is carried out by a backboard, the adopted lens is a 50mm prime lens, and the adopted filter is a 650nm high-pass filter. The excitation light source is a high-power air-cooled light source, the wavelength of a main light-emitting peak is 400nm, the transmittance of the optical filter is more than 90%, the excitation light is irradiated by a pulse mode and a continuous mode, the light source control signal is TTL, and the filtering combination mode is low-pass plus narrow-wave. The synchronous trigger can set the period, time delay, pulse width and pulse number of pulse signals to realize the time sequence control of camera exposure and excitation light source, and the time sequence control is a single-path input 8-path output, and the control precision is less than 10 nanoseconds. And the data processing industrial personal computer is connected with the synchronous trigger and the high-speed camera and is used for setting parameters of the synchronous trigger, further controlling the time sequence of irradiation of an excitation light source and exposure of the camera, receiving a light intensity image of the surface of the model shot by the camera, and performing image post-processing to obtain a required data map of the surface pressure of the model.
Referring to fig. 6, a method for improving the test efficiency of the pressure-sensitive paint technology comprises the following steps:
and S1, before the wind tunnel blowing is started, the exciting light is turned on, and the camera acquires the light image emitted by the PSP coating on the model surface at each attack angle, namely the reference image. After the collection is finished, the exciting light is turned off, and the camera collects dark images at each attack angle;
considering that the dark image and the reference image are acquired before the wind tunnel is blown to start, the blowing time and the test energy consumption are not increased, in order to weaken the random salt and pepper noise of the image, 10 reference images and 10 dark images are acquired according to the standard of the test process of the conventional PSP technology at each test attack angle, and are respectively subjected to average processing to obtain a dark image and a reference image.
S2, starting the wind tunnel, turning on exciting light after a flow field is stable, and collecting a light image emitted by a PSP coating on the surface of the model, namely a test image, by a camera at each attack angle;
in order to reduce the salt-pepper noise of the image, at least 10 test images are required to be acquired and subjected to average processing according to the standard of the conventional PSP technical test process, and considering that the test images are acquired after the wind tunnel is blown and started, the blowing time is increased due to the increase of the acquired number, so that the test efficiency is reduced, and the test cost is increased. In the step, only 1 test image is collected, so that the test efficiency is greatly improved, and the test cost is reduced.
S3, loading the dark image, the reference image and the test image collected by the camera, selecting the mark points, identifying the mark points and positioning the mark points on the reference image and the test image, and storing the positioned coordinate file of the mark points;
the mark points are black circles and are evenly distributed around the model image, and the number of the mark points is 12. The mark point is positioned by a mass center method, and the mass center position is obtained by the following formula:
wherein,
is the coordinate of the pixel point, I is the gray value of the pixel point,
is the mark point centroid.
S4, registering the test image to the position of the reference image according to the coordinate relation of the mark points, checking the registration precision, if the precision reaches the standard, storing the registered test image, entering the step S5, and if the precision does not reach the standard, returning to the step S3;
the method comprises the steps of carrying out image registration by adopting a point mapping method, extracting and positioning feature points in a reference image and a test image, fitting the mutual relation between the reference image and the test image to obtain the registration relation between the test image and the reference image, mapping gray values of pixel points of the test image one by one into the reference image by using the obtained registration relation, calculating the gray values of the integer pixel points by using an interpolation method after the mapped test image is not on the integer pixel points, and finishing the registration of the test image.
Further, the point mapping method is a method for image registration by using the correlation between corresponding mark points of two images, and the key is to find out the transformation relationship between the two images, which is generally divided into rigid transformation, affine transformation, projection transformation and nonlinear transformation. Considering that the train head model is high in rigidity and low in wind load, and the model moves wholly and deforms locally little before and after pneumatic blowing is started, the registration is carried out by adopting a rigid transformation method. Rigid transformation refers to a transformation method in which the distance between two points in one graph is still unchanged after transformation into the other graph, and rigid transformation can be decomposed into translation, rotation and mirror image. The rigid transformation relation is:
wherein x and y are the coordinates of the characteristic points of the images of the test sequence,
in order to refer to the coordinates of the feature points of the image,
is the angle of rotation,
is a translation vector.
S5, subtracting the background image from the reference image, subtracting the background image from the test image, and filling the image in the pressure hole area in the image to obtain a filled reference image and a filled test image;
firstly, selecting a processing area, wherein the area comprises 8 pressure taps on a train head model, filling an image of the processing area by adopting an interpolation method, the interpolation function is an algebraic polynomial, the interpolation method adopts Lagrange interpolation, and the Lagrange interpolation adopts the following form:
；
wherein,
in order to be a function of the polynomial interpolation,
in order to interpolate the basis functions of the image,
in order to know the gray value of the pixel,
in order to know the coordinates of the pixel points,
the rank of the known pixel is known. The interpolation basis function is of the form:
s6, setting the region outside the model as a background region, and not assigning the light intensity of the reference image and the test image in the background region so as to reduce the subsequent image processing time and improve the image processing efficiency, and filtering the image of the model region outside the background region to obtain the filtered reference image and the filtered test image;
firstly, setting a background region by adopting a relative threshold value method, and then carrying out Gaussian filtering processing on the model region image except the background region, wherein the adopted Gaussian filtering diameter is 30 pixels, and the iteration frequency is 15 times. The filtering function form of the Gaussian filtering method is as follows:
wherein x and y are pixel point coordinates,
the size of (d) determines the width of the gaussian function, taken to be 1.5.
And S7, carrying out ratio processing on the reference image and the test image to obtain a ratio image, converting the ratio image to obtain a train head surface pressure image according to a calibration relational expression of the pressure-sensitive paint light intensity ratio and the pressure obtained before the test, and further converting the pressure image to obtain a final pressure coefficient image according to the known wind tunnel inflow parameters.
The calibration coefficients of the pressure-sensitive paint used were obtained by special calibration tests before testing, the relationship being in the form:
wherein, P is the pressure,
in order to calibrate the coefficients for the calibration,
the ratio of the light intensity of the pressure-sensitive image to the light intensity of the reference image in the two states of blowing and not blowing is shown, and T is temperature.
Referring to fig. 9, the pressure-sensitive paint test method of the present invention optimizes the post-processing flow of the existing PSP technology image for a single test image, effectively avoids the influence of the background area on the image edge caused by the existing flow, improves the edge fidelity of the pressure result image, effectively reduces the salt and pepper noise of the single test image through large-scale and multiple gaussian filtering, and greatly improves the pressure post-processing precision of the single test image. As shown in the figures 7-8, by adopting the method, only 1 test image is needed to be acquired during the PSP wind tunnel test, so that the PSP measurement accuracy is equivalent to that of the conventional multi-image averaging method, meanwhile, the test blowing time can be shortened by 2/3, the test expenditure is saved by 66%, and the test efficiency and the economy are greatly improved. The method can effectively improve the test efficiency and greatly reduce the test cost on the premise of not reducing the measurement accuracy, and has higher engineering popularization and application value.
The above scheme is merely illustrative of a preferred example, and is not limiting. When the invention is implemented, appropriate replacement and/or modification can be carried out according to the requirements of users.
The number of apparatuses and the scale of the process described herein are intended to simplify the description of the present invention. Applications, modifications and variations of the present invention will be apparent to those skilled in the art.
While embodiments of the invention have been disclosed above, it is not intended to be limited to the uses set forth in the specification and examples. It can be applied to all kinds of fields suitable for the present invention. Additional modifications will readily occur to those skilled in the art. It is therefore intended that the invention not be limited to the exact details and illustrations described and illustrated herein, but fall within the scope of the appended claims and equivalents thereof.
Claims (8)
1. A method of increasing the efficiency of a pressure sensitive paint test, comprising:
before wind blowing of a wind tunnel is started, dark images and reference images of a model surface PSP coating at each attack angle are collected based on a camera by switching the working state of exciting light;
switching the working state of exciting light to be on after the wind tunnel is started, and acquiring a test image of the model surface PSP coating at each attack angle based on a camera;
thirdly, obtaining a corresponding ratio image based on the marking and Gaussian filtering processing of the dark image, the reference image and the test image;
step four, converting the ratio image to obtain a pressure image of the surface of the model based on a calibration relational expression of the light intensity ratio and the pressure of the pressure-sensitive paint obtained after the test;
converting the pressure image based on the known wind tunnel inflow parameters to obtain a corresponding pressure coefficient image;
in the first step, when acquiring a dark image and a reference image, respectively acquiring 10 initial dark images and 10 initial reference images at each attack angle through a camera, and respectively performing average processing on the 10 initial dark images and the 10 initial reference images to obtain a corresponding dark image and a corresponding reference image;
in step two, the test image is configured to collect only one corresponding test image at each angle of attack;
in step three, the gaussian filter diameter is configured to be 30 pixels, and the number of iterations is configured to be 15.
2. The method for improving the efficiency of a pressure sensitive paint test of claim 1, wherein in step three, the marking, treating is configured to include:
s30, loading the dark image, the reference image and the test image, and performing mark point selection, mark point identification and mark point positioning on the reference image and the test image to obtain a mark point coordinate file for storing and positioning;
s31, registering the test image to the position of the reference image based on the coordinate relation of the mark points, checking the registration precision, if the precision meets the preset requirement, storing the registered test image, and entering the step S32, if the precision does not meet the standard, returning to the step S30;
s32, respectively subtracting the background image from the reference image and the test image, and filling the image in the area without the photosensitive paint in the image to obtain a filled reference image I and a filled test image I;
s33, setting the region outside the model as a background region, wherein the light intensity of the reference image I and the test image I in the background region is not assigned, and performing Gaussian filtering on the model region image outside the background region to obtain a filtered reference image II and a filtered test image II;
and S34, carrying out ratio processing on the second reference image and the second test image to obtain a corresponding ratio image.
3. The method for improving the efficiency of pressure-sensitive paint testing as claimed in claim 2, wherein in step S30, the marking points are black circles uniformly distributed around the model image and have at least 12 marking points, and the marking point positions are configured to adopt a centroid method, a centroid method and a template-based cross-correlation method.
4. The method for improving the efficiency of the pressure-sensitive paint test according to claim 2, wherein in S31, the registration is configured to adopt a registration mode based on the characteristic information to fit the correlation between the reference image and the test image by extracting and positioning the characteristic points in the reference image and the test image, so as to obtain the registration relationship between the test image and the reference image;
mapping the gray values of pixel points of the test image one by one to a reference image based on the registration relation;
and (4) the mapped test image is not on the integer pixel point, the gray value on the integer pixel point is calculated by using an interpolation method, and the registration of the test image is finally completed.
5. The method of increasing the efficiency of a pressure sensitive paint test of claim 2, wherein in S32 the filling is configured to include:
s320, selecting a processing area which is to contain and exceed an area needing to be filled with the image;
s321, filling the processing area image by adopting an interpolation method, wherein an interpolation function in the interpolation method is an algebraic polynomial.
6. The method for improving the efficiency of a pressure sensitive paint test of claim 2, wherein in S33 the setting of the background area is configured to be obtained using a thresholding method;
the filter function form of the Gaussian filtering method is configured as follows:
wherein,
is the coordinate of the pixel point, and the coordinate of the pixel point,
is configured to be 1.5.
7. The method for improving the test efficiency of the pressure-sensitive paint according to claim 2, wherein in the fourth step, the light intensity ratio of the pressure-sensitive paint after test and the pressure are calibrated according to the following formula:
wherein, P is the pressure,
in order to calibrate the coefficients for the calibration,
the ratio of the light intensity of the pressure-sensitive image to the light intensity of the reference image in the two states of blowing and not blowing is shown, and T is temperature.
8. The method for improving the efficiency of a pressure sensitive paint test according to claim 7, wherein the obtaining of the pressure sensitive paint light intensity ratio and pressure calibration relation is configured to include:
s40, before the wind tunnel test, based on the parameter setting of the calibration box and the relevant software, obtaining a pre-test calibration fitting relational expression and a fitting curved surface of the pressure-sensitive paint coating under the conditions that the atmospheric pressure and the temperature at the calibration moment are used as reference pressure and temperature;
and S41, during the wind tunnel test, inversely calculating the coating light intensity ratio under the real reference condition based on the pre-test calibration fitting relational expression by actually measuring the atmospheric pressure and the ambient temperature in the tunnel, and obtaining the post-test calibration fitting relational expression and the fitting curved surface of the pressure-sensitive paint coating under the condition that the atmospheric pressure and the temperature in the tunnel during the wind tunnel test are taken as the reference conditions.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115343013A (en) * | 2022-10-18 | 2022-11-15 | 湖南第一师范学院 | Cavity Model Pressure Measurement Method and Related Equipment |
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| CN118533675A (en) * | 2024-07-19 | 2024-08-23 | 中国空气动力研究与发展中心低速空气动力研究所 | Impact resistance analysis method, computer program and storage medium for pressure sensitive paint |
| CN118675001A (en) * | 2024-08-13 | 2024-09-20 | 中国航空工业集团公司沈阳空气动力研究所 | PSP test system and method of moving aeroelastic model and data processing method |
Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5186046A (en) * | 1990-08-20 | 1993-02-16 | Board Of Regents Of The University Of Washington | Surface pressure measurement by oxygen quenching of luminescence |
| US5612492A (en) * | 1995-06-07 | 1997-03-18 | Mcdonnell Douglas Corporation | Formulations and method of use of pressure sensitive paint |
| US5731995A (en) * | 1996-04-12 | 1998-03-24 | Mc Donnell Douglas Corp | Method for determining pressure |
| US20030111615A1 (en) * | 2001-12-17 | 2003-06-19 | Benne Michael E. | Method and apparatus to correct for the temperature sensitivity of pressure sensitive paint |
| US20070105235A1 (en) * | 2005-02-16 | 2007-05-10 | Carlson William B | Osmium-based oxygen sensor and pressure-sensitive paint |
| CN104075870A (en) * | 2014-07-08 | 2014-10-01 | 中国空气动力研究与发展中心低速空气动力研究所 | Low-speed wind tunnel fluorescent silk thread based on fiber threads and flow visualization method thereof |
| CN104808012A (en) * | 2015-05-15 | 2015-07-29 | 北京航空航天大学 | A three-dimensional velocity-pressure coupling measurement method and system |
| IN2012DE01687A (en) * | 2012-06-01 | 2015-10-02 | Council Scient Ind Res | |
| CN105784220A (en) * | 2016-05-03 | 2016-07-20 | 中国空气动力研究与发展中心高速空气动力研究所 | Method for improving measurement accuracy of pressure-sensitive coating |
| CN107665266A (en) * | 2016-07-28 | 2018-02-06 | 中国空气动力研究与发展中心高速空气动力研究所 | The optimization method and device of pressure sensitive coating characterisitic parameter |
| CN107807418A (en) * | 2016-09-06 | 2018-03-16 | 迪睿合电子材料有限公司 | Optical component and window materials |
| CN108287052A (en) * | 2018-04-03 | 2018-07-17 | 华南理工大学 | A kind of complete and nominal enclosed construction wind-induced internal pressure test method |
| CN108318215A (en) * | 2018-02-08 | 2018-07-24 | 中国空气动力研究与发展中心高速空气动力研究所 | A kind of determination method of pressure sensitive paint trial image acquisition width number |
| CN110455489A (en) * | 2019-08-09 | 2019-11-15 | 东南大学 | A wind tunnel test device for measuring the wind pressure on the surface of the bridge segment vibration model |
| CN110702366A (en) * | 2019-11-01 | 2020-01-17 | 中国空气动力研究与发展中心超高速空气动力研究所 | Embedded optical pressure measurement method for shielding position of hypersonic wind tunnel model |
| CN110987360A (en) * | 2019-12-25 | 2020-04-10 | 中国空气动力研究与发展中心低速空气动力研究所 | Shock tube test system for PSP dynamic calibration device |
| CN111458101A (en) * | 2020-03-12 | 2020-07-28 | 中国空气动力研究与发展中心低速空气动力研究所 | Method for measuring surface pulsating pressure of wind tunnel fixed wing model |
| CN112945501A (en) * | 2021-02-03 | 2021-06-11 | 中国空气动力研究与发展中心高速空气动力研究所 | Laminar flow wing transition position measurement test method |
| CN113642681A (en) * | 2021-10-13 | 2021-11-12 | 中国空气动力研究与发展中心低速空气动力研究所 | Matching method of aircraft model surface mark points |
| CN113643371A (en) * | 2021-10-13 | 2021-11-12 | 中国空气动力研究与发展中心低速空气动力研究所 | Method for positioning aircraft model surface mark points |
| CN114119681A (en) * | 2021-11-12 | 2022-03-01 | 中国空气动力研究与发展中心高速空气动力研究所 | A cavity model pulsating pressure and noise measurement method, device and electronic equipment |
-
2022
- 2022-03-10 CN CN202210232341.6A patent/CN114323543B/en active Active
Patent Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5186046A (en) * | 1990-08-20 | 1993-02-16 | Board Of Regents Of The University Of Washington | Surface pressure measurement by oxygen quenching of luminescence |
| US5612492A (en) * | 1995-06-07 | 1997-03-18 | Mcdonnell Douglas Corporation | Formulations and method of use of pressure sensitive paint |
| US5731995A (en) * | 1996-04-12 | 1998-03-24 | Mc Donnell Douglas Corp | Method for determining pressure |
| US20030111615A1 (en) * | 2001-12-17 | 2003-06-19 | Benne Michael E. | Method and apparatus to correct for the temperature sensitivity of pressure sensitive paint |
| US20070105235A1 (en) * | 2005-02-16 | 2007-05-10 | Carlson William B | Osmium-based oxygen sensor and pressure-sensitive paint |
| IN2012DE01687A (en) * | 2012-06-01 | 2015-10-02 | Council Scient Ind Res | |
| CN104075870A (en) * | 2014-07-08 | 2014-10-01 | 中国空气动力研究与发展中心低速空气动力研究所 | Low-speed wind tunnel fluorescent silk thread based on fiber threads and flow visualization method thereof |
| CN104808012A (en) * | 2015-05-15 | 2015-07-29 | 北京航空航天大学 | A three-dimensional velocity-pressure coupling measurement method and system |
| CN105784220A (en) * | 2016-05-03 | 2016-07-20 | 中国空气动力研究与发展中心高速空气动力研究所 | Method for improving measurement accuracy of pressure-sensitive coating |
| CN107665266A (en) * | 2016-07-28 | 2018-02-06 | 中国空气动力研究与发展中心高速空气动力研究所 | The optimization method and device of pressure sensitive coating characterisitic parameter |
| CN107807418A (en) * | 2016-09-06 | 2018-03-16 | 迪睿合电子材料有限公司 | Optical component and window materials |
| CN108318215A (en) * | 2018-02-08 | 2018-07-24 | 中国空气动力研究与发展中心高速空气动力研究所 | A kind of determination method of pressure sensitive paint trial image acquisition width number |
| CN108287052A (en) * | 2018-04-03 | 2018-07-17 | 华南理工大学 | A kind of complete and nominal enclosed construction wind-induced internal pressure test method |
| CN110455489A (en) * | 2019-08-09 | 2019-11-15 | 东南大学 | A wind tunnel test device for measuring the wind pressure on the surface of the bridge segment vibration model |
| CN110702366A (en) * | 2019-11-01 | 2020-01-17 | 中国空气动力研究与发展中心超高速空气动力研究所 | Embedded optical pressure measurement method for shielding position of hypersonic wind tunnel model |
| CN110987360A (en) * | 2019-12-25 | 2020-04-10 | 中国空气动力研究与发展中心低速空气动力研究所 | Shock tube test system for PSP dynamic calibration device |
| CN111458101A (en) * | 2020-03-12 | 2020-07-28 | 中国空气动力研究与发展中心低速空气动力研究所 | Method for measuring surface pulsating pressure of wind tunnel fixed wing model |
| CN112945501A (en) * | 2021-02-03 | 2021-06-11 | 中国空气动力研究与发展中心高速空气动力研究所 | Laminar flow wing transition position measurement test method |
| CN113642681A (en) * | 2021-10-13 | 2021-11-12 | 中国空气动力研究与发展中心低速空气动力研究所 | Matching method of aircraft model surface mark points |
| CN113643371A (en) * | 2021-10-13 | 2021-11-12 | 中国空气动力研究与发展中心低速空气动力研究所 | Method for positioning aircraft model surface mark points |
| CN114119681A (en) * | 2021-11-12 | 2022-03-01 | 中国空气动力研究与发展中心高速空气动力研究所 | A cavity model pulsating pressure and noise measurement method, device and electronic equipment |
Non-Patent Citations (2)
| Title |
|---|
| YOSHIMI IIJIMA 等: "Development of Luminophore-Pendant Temperature-Sensitive Paint and its Application to Pressure-Sensitive Paint for Aerodynamic Measurements", 《22ND INTERNATIONAL CONGRESS ON INSTRUMENTATION IN AEROSPACE SIMULATION FACILITIES》 * |
| 张文清 等: "基于双核高斯滤波的PSP图像去噪", 《电子测试》 * |
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| CN117232395A (en) * | 2023-11-14 | 2023-12-15 | 中国空气动力研究与发展中心高速空气动力研究所 | Automatic recognition method for shock wave position of pressure sensitive paint image |
| CN117232395B (en) * | 2023-11-14 | 2024-01-23 | 中国空气动力研究与发展中心高速空气动力研究所 | Automatic recognition method for shock wave position of pressure sensitive paint image |
| CN117249967A (en) * | 2023-11-20 | 2023-12-19 | 中国空气动力研究与发展中心高速空气动力研究所 | Pressure and temperature synchronous measurement method based on two-component pressure-sensitive paint technology |
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| CN117409226B (en) * | 2023-12-15 | 2024-03-19 | 中国空气动力研究与发展中心高速空气动力研究所 | Automatic identification and registration method for pressure-sensitive paint image mark points |
| CN117990267A (en) * | 2024-04-07 | 2024-05-07 | 中国空气动力研究与发展中心低速空气动力研究所 | Method, system and medium for obtaining pressure sensitivity of pressure sensitive paint |
| CN118533675A (en) * | 2024-07-19 | 2024-08-23 | 中国空气动力研究与发展中心低速空气动力研究所 | Impact resistance analysis method, computer program and storage medium for pressure sensitive paint |
| CN118675001A (en) * | 2024-08-13 | 2024-09-20 | 中国航空工业集团公司沈阳空气动力研究所 | PSP test system and method of moving aeroelastic model and data processing method |
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