CN111473943A - PIV near-wall data processing method and device and storage medium - Google Patents
PIV near-wall data processing method and device and storage medium Download PDFInfo
- Publication number
- CN111473943A CN111473943A CN202010189379.0A CN202010189379A CN111473943A CN 111473943 A CN111473943 A CN 111473943A CN 202010189379 A CN202010189379 A CN 202010189379A CN 111473943 A CN111473943 A CN 111473943A
- Authority
- CN
- China
- Prior art keywords
- wall surface
- laser
- piv
- wall
- flow field
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000003672 processing method Methods 0.000 title claims description 16
- 238000003708 edge detection Methods 0.000 claims abstract description 30
- 238000012545 processing Methods 0.000 claims abstract description 25
- 239000002245 particle Substances 0.000 claims abstract description 22
- 239000000700 radioactive tracer Substances 0.000 claims abstract description 13
- 238000002474 experimental method Methods 0.000 claims abstract description 7
- 238000012360 testing method Methods 0.000 claims description 31
- 238000000034 method Methods 0.000 abstract description 20
- 238000000917 particle-image velocimetry Methods 0.000 description 57
- 230000035939 shock Effects 0.000 description 15
- 238000005516 engineering process Methods 0.000 description 10
- 238000000926 separation method Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 6
- 238000011144 upstream manufacturing Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000002485 combustion reaction Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000009434 installation Methods 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 239000012491 analyte Substances 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000007664 blowing Methods 0.000 description 2
- QXJJQWWVWRCVQT-UHFFFAOYSA-K calcium;sodium;phosphate Chemical compound [Na+].[Ca+2].[O-]P([O-])([O-])=O QXJJQWWVWRCVQT-UHFFFAOYSA-K 0.000 description 2
- 239000011248 coating agent Substances 0.000 description 2
- 238000000576 coating method Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000000717 retained effect Effects 0.000 description 2
- 239000004952 Polyamide Substances 0.000 description 1
- BQCADISMDOOEFD-UHFFFAOYSA-N Silver Chemical compound [Ag] BQCADISMDOOEFD-UHFFFAOYSA-N 0.000 description 1
- XKRFYHLGVUSROY-UHFFFAOYSA-N argon Substances [Ar] XKRFYHLGVUSROY-UHFFFAOYSA-N 0.000 description 1
- 229910052786 argon Inorganic materials 0.000 description 1
- -1 argon ion Chemical class 0.000 description 1
- 238000000149 argon plasma sintering Methods 0.000 description 1
- 230000008021 deposition Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 229920001109 fluorescent polymer Polymers 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000011900 installation process Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 229920002647 polyamide Polymers 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 229910052709 silver Inorganic materials 0.000 description 1
- 239000004332 silver Substances 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
- 239000007921 spray Substances 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M9/00—Aerodynamic testing; Arrangements in or on wind tunnels
- G01M9/06—Measuring arrangements specially adapted for aerodynamic testing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P5/00—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft
- G01P5/26—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft by measuring the direct influence of the streaming fluid on the properties of a detecting optical wave
-
- G06T5/70—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10064—Fluorescence image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30204—Marker
Abstract
The invention discloses a high-speed wind tunnel and a PIV near-wall data processing module and method thereof. The method comprises the following steps: starting a laser under the condition that no tracer particles exist in the wind tunnel experiment section, and capturing a first laser scattering image comprising a wall surface scattering bright line; gradually reducing the laser energy until the lowest laser energy is reached, and capturing a second laser scattering image comprising a wall surface scattering bright line under the lowest laser energy; performing edge detection on the second laser scattering image to determine the position of the wall surface; determining the position of a first layer of grid points on one side of an actual observation flow field near the wall surface in the PIV flow field according to the accurately determined wall surface position; and assigning the speed value of the first layer grid point on one side of the actually observed flow field to be zero. The method can eliminate the interference of error data on the first layer of grid points outside the actual flow field and near the inner wall surface of the flow field.
Description
Technical Field
The invention relates to the field of wind tunnel testing, in particular to a high-speed wind tunnel testing technology for flow field analysis of a high-speed aircraft, and specifically relates to a PIV near-wall data processing method and device applied to a high-speed wind tunnel.
Background
Due to the fact that the boundary layer is too thick and the interaction between the shock wave and the boundary layer is very serious due to high compressibility and low Reynolds number of airflow in the actual operation environment of the high-speed aircrafts (such as supersonic/hypersonic aircrafts and hypersonic missiles) such as supersonic/hypersonic speed aircrafts, and the like, and the special phenomena of the high-speed flow field such as the shock wave and the like can be researched only by a high-precision measurement technology.
The high-speed wind tunnel can be used for basic theoretical research of high-speed flow field aerodynamics and ground simulation experiments of high-speed aircrafts. The experimental analysis of high velocity flow fields using PIV (Particle Image Velocimetry) technology in hypersonic wind tunnels has been acknowledged by more and more researchers in this field. For example, in a wind tunnel test, Scheel et al perform a flow field test on fuel inlets with different aerodynamic profiles of a scramjet combustion chamber by using a PIV technology. Beresh et al use PIV techniques to experimentally analyze high-speed flow fields with an incoming flow Mach number of 5, and test unsteady separations in turbulent boundary layers and shock wave structures. Haertig et al apply PIV technology to shock wave measurement with very high incoming flow Mach number, and the shock tunnel measurement time is in the transient millisecond level.
With the rapid expansion of camera technology and laser technology, scientific research and engineering application have raised higher requirements for the accuracy that the PIV technology in wind tunnel can achieve. For example, when the PIV technology is applied to a high-speed wind tunnel, in order to enable trace particles in a flow field to have sufficient scattering intensity and ensure that a PIV image has a sufficiently high signal-to-noise ratio, the incident laser intensity is generally strong. However, when strong laser light irradiates the wall surface of the experimental model, strong scattering is generated at the wall surface, and the scattering causes a wide white bright line to be generated in the PIV image, and under certain test scenes, the interference caused by the white bright line may cause a completely wrong test result.
Disclosure of Invention
In one embodiment, the present invention provides a PIV near-wall data processing method for performing a flow field test on an object to be tested in a wind tunnel, particularly a high-speed wind tunnel, wherein the object to be tested has a wall surface, the method comprising the following steps:
starting a laser under the condition that no tracer particles exist in the wind tunnel experiment section, and capturing a first laser scattering image comprising a wall surface scattering bright line;
gradually reducing the laser energy until the lowest laser energy is reached, and capturing a second laser scattering image comprising a wall surface scattering bright line under the lowest laser energy;
performing edge detection on the second laser scattering image to determine the position of the wall surface;
determining the position of a first layer of grid points on one side of an actual observation flow field near the wall surface in the PIV flow field according to the accurately determined wall surface position;
and assigning the speed value of the first layer grid point on one side of the actually observed flow field to be zero.
Further, the lowest laser energy enables the wall scattering bright lines captured by the camera to be smaller than a preset value at the widest position.
Further, the lowest laser energy is such that the wall scattering bright lines captured by the camera are smaller than 2 pixels at the widest position.
And further, performing edge detection on the second laser scattering image by using a Canny edge detection operator to determine the position of the wall surface.
Further, when the scattering bright lines at the wall surface are discontinuous along the flow direction, the parameters of the profile position of the discontinuity are supplemented according to the profile function of the wall surface and the positions of the wall surface before discontinuity and the wall surface after discontinuity.
Further, the method also comprises a step of marking the first-layer grid point position index.
In another embodiment, the present invention further provides a PIV near-wall data processing apparatus for performing a flow field test on an object to be tested in a wind tunnel, wherein the object to be tested has a wall surface, the apparatus comprising:
the laser control unit is used for controlling the laser to be started and adjusting the laser energy emitted by the laser;
the image acquisition unit is used for capturing a laser scattering image generated by scattering the laser on the wall surface;
the laser control unit starts the laser device under the condition that no tracer particles exist in the wind tunnel experiment section, and gradually reduces the laser energy until the lowest laser energy is reached, wherein the lowest laser energy enables a wall surface scattering bright line to be smaller than a preset value at the widest position in a laser scattering image captured by the image acquisition unit;
the edge detection unit is used for carrying out edge detection on the laser scattering image so as to determine the accurate position of the wall surface;
the grid point determining unit is used for determining the position of a first layer of grid points on one side of an actual observation flow field near the wall surface in the PIV flow field according to the accurately determined wall surface position;
and the grid point marking unit is used for assigning the speed value of the first layer of grid points on one side of the actual observation flow field to be zero.
Further, the lowest laser energy enables the wall scattering bright lines to be smaller than 2 pixels at the widest position in the laser scattering image captured by the image acquisition unit.
Further, the device also comprises a wall surface intermittent filling unit, and the wall surface intermittent filling unit can fill the profile position parameters of the intermittent part according to the profile function of the wall surface and the position of the front intermittent wall surface and the position of the rear intermittent wall surface determined by the edge detection unit.
In further embodiments, the invention also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, configure the processor to perform the PIV near-wall data processing method of any of the above aspects.
In another embodiment, the invention further provides a wind tunnel, in particular a high-speed wind tunnel, which includes the PIV near-wall data processing module according to any one of the above technical solutions.
In the PIV flow field data processed by the technical scheme, the speed data and the data outside the flow field on the first layer grid on the near wall in the flow field can be eliminated in the further processing of the PIV flow field data, namely the speed data and the data outside the flow field on the first layer grid on the near wall in the flow field are not used, so that the interference of error data on the first layer grid points outside the actual flow field and near the inner wall surface of the flow field can be eliminated.
Drawings
FIG. 1 is a schematic diagram of a wind tunnel configuration with a PIV test system according to one embodiment of the present invention;
FIG. 2 is a PIV digital image obtained in a PIV test;
FIG. 3 is a schematic diagram of query region partitioning in PIV cross-correlation calculation;
FIG. 4 is a flow diagram of a PIV near-wall data processing method according to one embodiment of the invention;
FIG. 5 is a partially enlarged view of a bright scattering line generated at a wall surface by laser light;
FIG. 6 is a schematic view of the wall profile at the discontinuity;
FIG. 7 is a schematic diagram showing the positions of grid points of a near-wall first layer in a flow field;
FIG. 8 is a schematic diagram of a PIV near-wall data processing module configuration according to an embodiment of the invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings.
The high-speed wind tunnel according to the present invention may be, for example, a hypersonic wind tunnel, a supersonic wind tunnel, a subsonic wind tunnel, or the like. In addition, in some cases, the disclosed wind tunnel structure is also suitable for low speed wind tunnels.
In some embodiments, the present invention may be used in the test of supersonic speed, hypersonic speed, and other flow field environments, such as supersonic combustion ramjet engine intake and exhaust systems, internal and external flow integrated aerodynamic layout design, and the like, where the airflow in the above environments is a highly non-uniform, unusual and complex three-dimensional flow field, and the flow field has shock waves, slip surfaces, and separation shear layers with great flow parameter variation gradients, which involve a great deal of interactions with shock waves/shock waves, shock waves/expansion waves, shock waves/boundary layers, and thus complex phenomena such as boundary layer separation, large-scale separation vortices, and recirculation zones.
In some embodiments, the high-speed wind tunnel comprises a blowing and sucking wind tunnel, and mainly comprises an experimental section, a high-pressure source connected to the upstream of the experimental section, and a low-pressure source connected to the downstream of the experimental section.
FIG. 1 illustrates a high speed wind tunnel according to one embodiment of the present invention. As shown in FIG. 1, the high velocity wind tunnel includes a test section. In other embodiments, a nozzle (not shown) may be connected to the upstream end of the experimental section and a diffuser (not shown) may be connected to the downstream end of the experimental section, and a high-pressure air source is arranged upstream of the experimental section and a low-pressure air source is arranged downstream of the experimental section, so as to form a blowing-sucking wind tunnel. An analyte such as an experimental model or the like is fixed in the experimental section 1. The Mach number operating range of the wind tunnel is 0.5-6. In some cases, the wind tunnel may also operate below 0.5, and above mach number 7.
And a tracing particle generator (not shown), a laser, an optical path system, a digital camera, an image acquisition and processing system and the like are arranged around the experimental section.
In certain embodiments, the tracer particles can be, for example, polyamide tracer particles (PSP), Hollow Glass Spheres (HGS), silver coated Hollow Glass Spheres (S-HGS), Fluorescent Polymer Particles (FPP), and the like.
The laser light source system comprises a laser generator, a light guide arm, a sheet light source emission lens and the like. The light guide arm can transmit the strong light energy emitted by the pulse laser to a required position efficiently, safely and flexibly. The sheet-light source emission lens (e.g., comprising a cylindrical mirror and a spherical mirror) converts the cylindrical light beam into a fan-shaped sheet of light that illuminates a predetermined area at a specific location in the flow field. In some cases, the light guide arm can be omitted, and the laser is emitted and then directly converted into the sheet light source through the sheet light source emitting lens.
The laser may be a continuous laser or a pulsed laser, such as an argon ion laser, Nd: YAG pulse laser.
In the process of measuring a flow field by adopting a Particle Image Velocimetry (PIV), trace particles put into the flow field are illuminated at least twice on the same flow plane by high-intensity laser sheet light within a very short time interval, light scattering generated by the particles is recorded by a high-resolution digital camera, and the moving distance of the trace particles between two illuminations represents the moving distance of fluid. When the wind tunnel is used for testing a logistics field to be tested, the particle broadcaster is started firstly, the wind tunnel is operated, and then the laser sheet light is started to illuminate the flow field. A digital camera, such as a CCD video camera, takes an image of the flow field PIV through the side window. And analyzing and processing the PIV image by using an image processing unit to obtain the flow field parameters, such as the flow field velocity.
The image processing unit comprises a data processing module, the data processing module divides the digital PIV image into a plurality of sub-regions called 'query regions', and the local displacement vector deltas of the tracer particles in each query region in the cross-frame time can be determined by performing cross-correlation processing on two adjacent time-correlated images. Further, the velocity distribution in the flow field can be determined according to the cross-frame time delta t and the magnification factor k of the image: and u is k delta s/delta t.
The method is only suitable for the condition that no wall surface exists in the observation flow field, if the wall surface exists in the observation flow field, especially a complex wall surface, such as the surface of an array vortex generator, a corrugated wall surface and the like, the near-wall data needs to be further processed, otherwise, a large error is brought.
When the shape of the wall surface is complicated and it is difficult to exclude the wall surface from the observation region, there is a problem that: when the PIV test is carried out in a wind tunnel, in order to ensure that the tracer particles in the flow field have enough scattering intensity and ensure that the PIV image has enough high signal-to-noise ratio, the incident laser intensity is generally stronger. However, when the stronger laser beam is irradiated onto the wall surface of the object to be measured, strong scattering occurs at the wall surface, as shown by the wider white bright line in fig. 2. Although some people choose to dispose a coating on the surface of the dut to reduce the scattering, the deposition of the coating on the surface of the dut may cause errors between the test value and the actual value, which may result in a decrease in the reliability of the test result.
In general, the wall position can be determined from the light scattered by the PIV image on the wall. However, when the scattered light is strong on the wall surface, the white bright line generated on the wall surface tends to be wide, as shown in fig. 2. Thus, there is a large error in determining the wall surface position from the white bright line on the wall surface, and it is difficult to accurately determine the wall surface position. Particularly, when the boundary layer of an object to be measured, such as a supersonic and hypersonic aircraft, is studied, it is very important to accurately determine the position of the wall surface because the thickness of the boundary layer is relatively thin.
Referring to FIG. 3, assume that the number of pixels in the PIV image is Nx×NyIn which N isxAnd NyThe pixel points of the PIV image in the horizontal direction and the vertical direction are respectively. In the cross-correlation calculation of the PIV image, the PIV image is divided into a plurality of the query areas, and the number of pixel points of each query area is mx×my. Each query region corresponds to a velocity vector in the flow field, such as vector u shown in fig. 3, by cross-correlation calculations.
As shown in fig. 3, for a complex curved wall W, the wall may cross multiple query regions and participate in the cross-correlation computation of the PIV image, as indicated by the dark labeled regions in fig. 3.
Because the wall surface is a non-flow field area, the scattering information of the laser at the wall surface is involved in PIV calculation, and the wrong velocity vector is necessarily obtained at a corresponding position. Therefore, if the flow field data obtained by directly using the PIV is not further processed on the PIV data near the wall surface in the actual flow field, the test error at the wall surface will be very large, and even completely wrong test data is obtained.
To solve the above problems, according to an embodiment of the present invention, a PIV near-wall data processing method is provided. As shown in fig. 4, the method includes the steps of:
step1, installing an object to be tested and PIV testing equipment in a wind tunnel according to testing requirements.
The installation of the object to be tested and the PIV test equipment belongs to the conventional technology in the field, and is not described in detail in the invention. During the installation process, the positions of the test devices and the object to be tested are kept unchanged. Especially, the installation positions of a laser light emitting head of the laser and the digital camera are ensured to be unchanged. The laser light emitting head is used for emitting high-energy plane laser, and the digital camera is used for capturing instantaneous laser scattering images.
And 2, starting a laser under the condition that no tracer particles exist in the wind tunnel experiment section, and capturing a first laser scattering image comprising a wall surface scattering bright line.
In this step, no tracer particles are scattered into the observed flow field.
When the laser is turned on, because there is no trace particle scattering, only bright lines generated by the scattering of the laser at the wall surface can be observed in the laser scattering image captured by the camera, as shown in fig. 5, for a complex wall surface, because there is a certain difference in the scattering light along the wall surface at different positions, the width of the laser scattering bright lines at different positions along the scattering bright lines, that is, the number of pixel points L occupied by the bright linesyThere will also be some differences.
And 3, gradually reducing the laser energy until the lowest laser energy is reached, wherein the lowest laser energy enables the wall scattering bright lines captured by the camera to be smaller than a preset value at the widest position, and capturing a second laser scattering image comprising the wall scattering bright lines under the lowest laser energy.
According to an alternative embodiment, the invention gradually reduces the emission energy of the laser during this step until the width of the wall scattering bright line in the laser scatter image captured by the camera does not exceed two pixels, i.e. L at all positions along the bright lineyLess than or equal to 2. The invention can ensure the determination precision of the wall surface position by reducing the intensity of the scattered laser and the width of the scattered light bright line at the wall surface.
And 4, carrying out edge detection on the second laser scattering image to determine the position of the wall surface.
And (3) aiming at the second laser scattering image which is captured under the lowest laser energy and comprises the wall scattering bright lines in the step (3), edge detection is carried out by using a Canny edge detection operator proposed by John nF.
And 3, because the laser scattering bright lines on the wall surface in the second laser scattering image captured in the step3 occupy less pixel points, wall surface position information with very high precision can be obtained.
Canny edge detection operator is a multi-level edge detection algorithm developed by john nf.
The method for realizing the edge detection process by using the Canny algorithm comprises the following steps:
step1, graying image
The Canny algorithm usually processes images as grayscale images, so if the camera acquires color images, graying is performed first. Graying a color image, namely carrying out weighted average according to sampling values of all channels of the image.
Taking color images in RGB format as an example, the method generally used for graying mainly includes:
the method comprises the following steps: (R + G + B)/3;
the method 2 comprises the following steps: gray ═ 0.299R +0.587G +0.114B (1)
Step2, denoising
The presence of noise affects the detection of edges, so that the image needs to be gaussian denoised to filter out high frequency noise.
Step3, solving gradient amplitude and direction
The magnitude and direction of the gradient can be solved using sobel operator (sobelopersor) operator. The sobel operator is one of operators in image processing, and is mainly used for realizing edge detection. The operator is a discrete difference operator for computing an approximation of the gradient of the image intensity function. Using this operator at any point in the image will produce a corresponding gradient vector or normal vector.
The operator comprises two sets of 3 × 3 matrices, horizontal and vertical, respectively, which are plane-convolved with the image to obtain horizontal and vertical luminance difference approximations.
If A represents the original image, GxAnd GyRepresenting the transverse and longitudinal edge detected images, respectively, then:
the lateral and longitudinal gradient approximations for each pixel of the image may be combined to calculate the magnitude of the gradient by:
the gradient direction can then be calculated using the following formula:
in the above example, if the above angle Θ is equal to zero, it means that the image has a longitudinal edge there.
Step4, non-maximum suppression
In the obtained amplitude image, there may be a plurality of cases where the larger amplitudes are close, but there is only one true edge point. In such a case, the local maximum value can be found by suppressing the non-maximum value, so that most of the non-edge points can be eliminated.
Therefore, each pixel point is processed as follows:
and determining the positions of adjacent pixel points to be compared according to the gradient direction of the pixel points. For example, a graphic composed of a straight line with a gradient direction of a pixel point and a plurality of pixel points adjacent to the pixel point is used for estimating an intersection pixel, if the pixel points are all larger than the intersection pixel, the pixel point is a maximum value edge point, and if not, the pixel point is a non-edge point.
Step5, detecting and connecting edges by using double-threshold algorithm
A hysteresis threshold is used to determine whether the pixel is an edge pixel. The hysteresis threshold includes two thresholds, a high threshold and a low threshold.
If the magnitude of a pixel location exceeds a high threshold, the pixel is retained as an edge pixel.
If the magnitude of a pixel location is less than the low threshold, the pixel is excluded.
If the magnitude of a pixel location is between the high and low thresholds, the pixel is only retained when connected to a pixel above the high threshold.
The ratio of the high and low thresholds is between 2:1 and 3: 1.
Although the present invention is described with respect to edge detection using the Canny edge detector, it will be appreciated by those skilled in the art that other known edge detection methods may be used to determine the wall location.
In some cases, the scattered light at the entire wall is continuous along the flow direction without significant discontinuities, and the wall position determined in this step is the geometric position of the wall within the entire observation area.
In other cases, discontinuities in the scattered light at the walls along the flow direction may occur, as shown in fig. 6. In this case, it is difficult to directly determine the wall surface position in part of the positions because the scattered light intensity is too weak.
X as shown in FIG. 61～x2In the area between the profile function f (x) of the wall and the position (x) of the wall before interruption determined in step4, without directly determining the position of the wall in this step1,y1) And the position (x) of the interrupted rear wall surface2,y2) The position parameters of the molded surface at the interval can be supplemented.
And 5, determining the position of a first layer of grid points on one side of the actually observed flow field near the wall surface in the PIV flow field according to the accurately determined wall surface position.
Wherein a grid in the flow field corresponds to the query region in the PIV cross-correlation calculation.
And 6, assigning the speed value of the first layer of grid points on one side of the actually observed flow field to be zero, and marking the position index of the first layer of grid points.
The velocity value of the first layer grid point on the side of the actually observed flow field is assigned to zero, and the position index of the first layer grid point is marked as NW (i, j), as shown in fig. 7.
And on the side of the non-observed flow field, assigning the velocity values on all grid points to be zero.
In the PIV flow field data processed by the method, the speed data and the data outside the flow field on the first layer grid on the near wall in the flow field can be eliminated in the further processing of the PIV flow field data, namely the speed data and the data outside the flow field on the first layer grid on the near wall in the flow field are not used, so that the interference of error data on the first layer grid point outside the actual flow field and near the inner wall surface of the flow field can be eliminated.
If the position of the camera or the laser light emitting head is adjusted or moved in the PIV testing process, the wall surface position needs to be determined again by adopting the steps according to the installation position of the testing equipment.
As shown in fig. 8, according to another embodiment of the present invention, a data processing apparatus for implementing the above-described PIV near-wall data processing method includes:
and the laser control unit is used for controlling the laser to be started and adjusting the laser energy emitted by the laser.
And the image acquisition unit is used for capturing a laser scattering image generated by scattering the laser on the wall surface.
The laser control unit starts the laser device in a wind tunnel experiment section under the condition of no tracer particles, and the image acquisition unit captures a first laser scattering image comprising a wall surface scattering bright line under the condition of no tracer particles.
The laser control unit gradually reduces the laser energy until the lowest laser energy is reached, and the lowest laser energy enables the wall scattering bright lines to be smaller than a preset value at the widest position in the laser scattering image captured by the image acquisition unit, namely the image acquisition unit captures a second laser scattering image comprising the wall scattering bright lines under the lowest laser energy.
And the edge detection unit carries out edge detection on the second laser scattering image so as to determine the accurate position of the wall surface.
According to an embodiment of the invention, edge detection is performed on the second laser scatter image using, for example, a Canny edge detection operator proposed by john nf.
In some cases, the scattered light at the entire wall is continuous along the flow direction without significant discontinuities, and the wall position determined in this step is the geometric position of the wall within the entire observation area.
In other cases, for example, where the wall portion is located too weakly due to the intensity of scattered light, discontinuity of the scattered light at the wall in the flow direction may occur, as shown in fig. 6. In this case, it is difficult for the edge detection unit to directly determine the wall surface position.
In order to solve the problem, the invention also comprises a wall surface discontinuity filling unit which can fill the profile position parameter of the discontinuity according to the profile function of the wall surface and the position of the wall surface before discontinuity and the position of the wall surface after discontinuity determined by the edge detection unit.
X as shown in FIG. 61～x2The area between the two areas, the position of the wall surface can not be determined directly by the edge detection unit, at the moment, the discontinuous wall surface filling unit determines the position (x) of the discontinuous front wall surface according to the profile function f (x) of the wall surface and the edge detection unit1,y1) And the position (x) of the interrupted rear wall surface2,y2) And (4) supplementing the profile position parameters of the intermittent parts.
And the grid point determining unit is used for determining the position of a first layer of grid points on one side of an actual observation flow field near the wall surface in the PIV flow field according to the accurately determined wall surface position.
Wherein a grid in the flow field corresponds to the query region in the PIV cross-correlation calculation.
And the grid point marking unit is used for assigning the speed value of the first layer of grid points on one side of the actual observation flow field to be zero and marking the position index of the first layer of grid points.
Specifically, the grid point labeling unit assigns a velocity value of the first-layer grid point on the side of the actually observed flow field to zero, and labels the position index of the first-layer grid point as NW (i, j), as shown in fig. 6.
Meanwhile, on the side of the non-observed flow field, the grid point marking unit assigns the speed values on all grid points to zero.
In the PIV flow field data processed by the data processing module, the speed data on the first layer grid on the near wall in the flow field and the data outside the flow field can be eliminated in the further processing of the PIV flow field data, namely the speed data on the first layer grid on the near wall in the flow field and the data outside the flow field are not used, so that the interference of error data on the first layer grid point outside the actual flow field and near the inner wall surface of the flow field can be eliminated.
According to yet another embodiment of the present invention, there is also provided a computer readable storage medium including a program or programs stored thereon, the program or programs including instructions which, when executed by a computer including a plurality of application programs, cause the computer to perform the PIV near-wall data processing method shown in any of the above embodiments.
According to yet another embodiment of the present invention, there is also provided a computer including the computer-readable storage medium described above, the computer including the PIV near-wall data processing module shown in any of the above embodiments.
Those skilled in the art will appreciate that in the embodiments provided by the present invention, the disclosed apparatus, devices, and methods may be implemented in other ways. For example, the above-described data processing module embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts indicated as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a portable terminal, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
According to another embodiment of the invention, the invention further provides a wind tunnel comprising the PIV near-wall data processing module, wherein the wind tunnel can be a hypersonic wind tunnel, a supersonic wind tunnel, a subsonic wind tunnel and the like.
Furthermore, in some cases, the wind tunnel may be a low-speed wind tunnel.
In addition, in some cases, the wind tunnel is used for supersonic speed, hypersonic speed and other flow field environment tests, such as supersonic speed combustion ramjet engine intake and exhaust systems, internal and external flow integrated aerodynamic layout design and the like, the airflow of the environment is a highly non-uniform and unusual complex three-dimensional flow field, shock waves, slip surfaces and separation shear layers with great flow parameter variation gradients exist in the flow field, and a great deal of complex phenomena such as shock waves/shock waves, shock waves/expansion waves and shock waves/boundary layers interaction, boundary layer separation, large-scale separation vortexes, backflow zones and the like are involved.
In addition, in some cases, the high-speed wind tunnel comprises a blowing and sucking wind tunnel which mainly comprises an experimental section, a high-pressure source connected to the upstream of the experimental section and a low-pressure air source connected to the downstream of the experimental section.
In one embodiment of the invention, the wind tunnel comprises a test section, the upstream end of the test section can be connected with a spray pipe and the downstream end of the test section can be connected with a diffuser. The upstream of the experimental section is provided with a high-pressure air source, and the downstream is provided with a low-pressure air source, so that a blowing-sucking wind tunnel is formed. The analyte such as an experimental model or the like is fixed in the experimental section.
The Mach number operating range of the wind tunnel is 0.5-6. In some cases, the wind tunnel may also operate below 0.5, and above mach number 7.
And a tracing particle generator, a laser, a light path system, a digital camera, an image acquisition and processing system and the like are arranged around the experimental section.
Although the embodiments of the present invention have been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the embodiments of the present invention.
Claims (10)
1. A PIV near-wall data processing method is used for carrying out flow field test on an object to be tested in a wind tunnel, wherein the object to be tested is provided with a wall surface, and is characterized by comprising the following steps:
starting a laser under the condition that no tracer particles exist in the wind tunnel experiment section, and capturing a first laser scattering image comprising a wall surface scattering bright line;
gradually reducing the laser energy until the lowest laser energy is reached, and capturing a second laser scattering image comprising a wall surface scattering bright line under the lowest laser energy;
performing edge detection on the second laser scattering image to determine the position of the wall surface;
determining the position of a first layer of grid points on one side of an actual observation flow field near the wall surface in the PIV flow field according to the accurately determined wall surface position;
and assigning the speed value of the first layer grid point on one side of the actually observed flow field to be zero.
2. The PIV near-wall data processing method of claim 1, wherein the lowest laser energy is such that a wall-scattering bright line captured by the camera is less than a predetermined value at the widest.
3. The PIV near-wall data processing method of claim 1 or 2, wherein the lowest laser energy is such that a wall-scattering bright line captured by a camera is less than 2 pixels at the widest.
4. The PIV nearwall data processing method of claim 1, wherein edge detection is performed on the second laser scatter image using a Canny edge detection operator to determine wall locations.
5. The PIV near-wall data processing method of claim 1 or 4, wherein when the scattering bright lines at the wall surface are discontinuous along the flow direction, the parameters of the profile position of the discontinuity are supplemented according to the profile function of the wall surface and the profile position of the discontinuity between the wall surface position before discontinuity and the wall surface position after discontinuity.
6. The PIV near-wall data processing method of any preceding claim, further comprising the step of marking the first layer grid point location index.
7. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, configure the processor to perform the PIV near-wall data processing method of any of claims 1-6.
8. A PIV near-wall data processing device is used for carrying out flow field test on an object to be tested in a wind tunnel, wherein the object to be tested is provided with a wall surface, and the device is characterized by comprising:
the laser control unit is used for controlling the laser to be started and adjusting the laser energy emitted by the laser;
the image acquisition unit is used for capturing a laser scattering image generated by scattering the laser on the wall surface;
the laser control unit starts the laser device under the condition that no tracer particles exist in the wind tunnel experiment section, and gradually reduces the laser energy until the lowest laser energy is reached, wherein the lowest laser energy enables a wall surface scattering bright line to be smaller than a preset value at the widest position in a laser scattering image captured by the image acquisition unit;
the edge detection unit is used for carrying out edge detection on the laser scattering image so as to determine the accurate position of the wall surface;
the grid point determining unit is used for determining the position of a first layer of grid points on one side of an actual observation flow field near the wall surface in the PIV flow field according to the accurately determined wall surface position;
and the grid point marking unit is used for assigning the speed value of the first layer of grid points on one side of the actual observation flow field to be zero.
9. The PIV near-wall data processing apparatus of claim 8, wherein the lowest laser energy is such that a wall scattering bright line is less than 2 pixels at a widest point in a laser scattering image captured by the image capture unit.
10. The PIV near-wall data processing apparatus according to claim 8 or 9, further comprising a wall surface discontinuity-filling unit capable of filling a profile position parameter of a discontinuity on the basis of the profile function of the wall surface and the locations of the wall surface before discontinuity and the wall surface after discontinuity determined by the edge detection unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010189379.0A CN111473943B (en) | 2020-03-18 | 2020-03-18 | PIV near-wall data processing method and device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010189379.0A CN111473943B (en) | 2020-03-18 | 2020-03-18 | PIV near-wall data processing method and device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111473943A true CN111473943A (en) | 2020-07-31 |
| CN111473943B CN111473943B (en) | 2022-02-22 |
Family
ID=71747517
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010189379.0A Active CN111473943B (en) | 2020-03-18 | 2020-03-18 | PIV near-wall data processing method and device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111473943B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113341174A (en) * | 2021-06-03 | 2021-09-03 | 中国人民解放军海军工程大学 | Transonic cascade wind tunnel piv test device test method and system |
| CN114459727A (en) * | 2021-12-30 | 2022-05-10 | 中国人民解放军国防科技大学 | Experimental system and method for SWTBLI unsteady characteristic research |
| CN116989977A (en) * | 2023-08-28 | 2023-11-03 | 哈尔滨工业大学 | Non-intervention type large-scale flow characteristic measurement method and measurement system in natural wind tunnel |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102393193A (en) * | 2011-10-12 | 2012-03-28 | 清华大学 | High frequency image acquisition system for measuring flow rate |
| CN102706529A (en) * | 2012-06-08 | 2012-10-03 | 中国人民解放军国防科学技术大学 | Method for calibrating and measuring supersonic flow field density field |
| CN105004466A (en) * | 2015-08-05 | 2015-10-28 | 北京航空航天大学 | High-precision non-contact pneumatic frictional resistance measuring method and measuring device |
| CN108680359A (en) * | 2018-06-22 | 2018-10-19 | 中国人民解放军战略支援部队航天工程大学 | Air-flow momentum gain measurement system and its application method and application |
-
2020
- 2020-03-18 CN CN202010189379.0A patent/CN111473943B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102393193A (en) * | 2011-10-12 | 2012-03-28 | 清华大学 | High frequency image acquisition system for measuring flow rate |
| CN102706529A (en) * | 2012-06-08 | 2012-10-03 | 中国人民解放军国防科学技术大学 | Method for calibrating and measuring supersonic flow field density field |
| CN105004466A (en) * | 2015-08-05 | 2015-10-28 | 北京航空航天大学 | High-precision non-contact pneumatic frictional resistance measuring method and measuring device |
| CN108680359A (en) * | 2018-06-22 | 2018-10-19 | 中国人民解放军战略支援部队航天工程大学 | Air-flow momentum gain measurement system and its application method and application |
Non-Patent Citations (2)
| Title |
|---|
| 付佳等: "高超声速钝头体表面超声速气膜冷却的试验研究", 《第十五届全国激波与激波管学术会议论文》 * |
| 王前程: "超声速边界层流向曲率效应研究", 《中国博士学位论文全文数据库(电子期刊)》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113341174A (en) * | 2021-06-03 | 2021-09-03 | 中国人民解放军海军工程大学 | Transonic cascade wind tunnel piv test device test method and system |
| CN114459727A (en) * | 2021-12-30 | 2022-05-10 | 中国人民解放军国防科技大学 | Experimental system and method for SWTBLI unsteady characteristic research |
| CN116989977A (en) * | 2023-08-28 | 2023-11-03 | 哈尔滨工业大学 | Non-intervention type large-scale flow characteristic measurement method and measurement system in natural wind tunnel |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111473943B (en) | 2022-02-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111473943B (en) | PIV near-wall data processing method and device and storage medium | |
| Lei et al. | A novel tunnel-lining crack recognition system based on digital image technology | |
| CN102435411B (en) | Full field measuring system and method of reynolds stress of NPLS compressible turbulent flow | |
| Kegerise et al. | An experimental investigation of a wing-fuselage junction model in the NASA Langley 14-by 22-foot subsonic wind tunnel | |
| Ghaemi et al. | Turbulent structure of high-amplitude pressure peaks within the turbulent boundary layer | |
| CN108593958B (en) | Method and device for synchronously acquiring gas-solid two-phase flow velocity field | |
| CA2322721A1 (en) | A system and method for detecting defects in a surface of a workpiece | |
| Duncan et al. | Effects of step excrescences on a swept wing in a low-disturbance wind tunnel | |
| CN111024980B (en) | Image velocimetry method for chromatographic particles near free interface | |
| CN111241667A (en) | Method for recognizing plasma configuration based on image processing and probe data processing | |
| Zhou et al. | UAV vision detection method for crane surface cracks based on Faster R-CNN and image segmentation | |
| Lu et al. | Vision-based structural displacement measurement under ambient-light changes via deep learning and digital image processing | |
| CN110044919A (en) | A kind of detection device and its detection method for mirror surfaces scratch | |
| CN105335764B (en) | A kind of docking aircraft model identification verification system and method | |
| CN106875427B (en) | Method for monitoring snaking motion of locomotive | |
| Loktev et al. | Image Blur Simulation for the Estimation of the Behavior of Real Objects by Monitoring Systems. | |
| Kushner et al. | Planning Image-based Measurements in Wind Tunnels by virtual imaging | |
| CN115901178A (en) | System and method for measuring and analyzing wave resonance flow field characteristics among multi-body marine structures | |
| CN113432829B (en) | PIV (particle image velocimetry) measurement integrated system for gas, solid and liquid multiphase cloud blasting turbulent flow field | |
| CN209048126U (en) | A kind of three-dimensional flow speed dynamic monitor | |
| CN111473944B (en) | PIV data correction method and device for observing complex wall surface in flow field | |
| JPH1031030A (en) | Fluid measuring method | |
| CN109044325B (en) | Three-dimensional blood flow velocity dynamic monitoring device and method | |
| Meyers et al. | Three component Doppler global velocimeter measurements of the flow above a delta wing | |
| CN111553876A (en) | Pneumatic optical sight error image processing method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |