A wind tunnel three-dimensional flow field non-interference measuring device and intelligent reconstruction method

Abstract

The application discloses a wind tunnel three-dimensional flow field non-interference measuring device and an intelligent reconstruction method, which comprises a light signal emitting array, a light signal receiving array, a light signal emitting system and a light signal processing system; a plurality of point light sources are arranged on the light signal emitting array; a plurality of light signal receivers are arranged on the light signal receiving array; the emitting direction angle of each point light source can be adjusted, and the emitted light of each point light source can be directed to any light signal receiver. In the application, the point light sources of the array and the light signal receivers of the array emit light signals in an optimized light signal path order. Through the mapping relationship between the current emitted light signal path and the actually received light signal receiver and the actual transmission time of each path, combined with the light refraction principle, the air density, speed, static pressure and dynamic pressure and other parameters of each grid point in the wind tunnel three-dimensional space can be calculated, and a visual wind tunnel three-dimensional flow field cloud picture can be drawn.

Description

Technical Field

[0001] This invention relates to the field of wind tunnel testing technology, and in particular to a non-interference measurement device and intelligent reconstruction method for three-dimensional flow field in a wind tunnel. Background Technology

[0002] Wind tunnel testing is a crucial method for studying structural wind engineering. Accurate measurement of flow field characteristics during testing is essential for understanding aerodynamic phenomena and validating theoretical models. Currently, common flow field visualization and measurement methods mainly include the smoke flow method and particle image velocimetry (PIV).

[0003] The smoke flow method releases smoke through specially designed smoke pipes or the surface of a model to display the flow pattern of gas around the model, allowing for a relatively intuitive observation of the fluid evolution. The PIV method requires the placement of tracer particles in the flow field and the use of lasers and high-speed cameras to record the particle motion, thereby analyzing the flow field characteristics near the particle trajectories. When the released smoke or the number of tracer particles is small, or the particles themselves are small, the smoke or tracer particles are considered to have minimal interference with the flow field. However, in actual wind tunnel testing, the following shortcomings still exist and require improvement:

[0004] 1. The arrangement of smoke streams or tracer particles will increase the fluid density in some areas of the wind tunnel wind field, which may cause the dynamic response of the tested structure or device to be greater than expected.

[0005] 2. Too few smoke streams or tracer particles will increase the difficulty of measurement, while too many will have a non-negligible impact on the flow field.

[0006] 3. The smoke flow method and the PIV method are mostly applicable to measuring the characteristics of wind tunnel wind field on two-dimensional slices. That is, they can only obtain flow information within a certain laser sheet plane, and it is difficult to obtain the three-dimensional wind field characteristics of the wind tunnel. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to address the shortcomings of the prior art by providing a wind tunnel three-dimensional flow field non-interference measurement device and intelligent reconstruction method. The wind tunnel three-dimensional flow field non-interference measurement device and intelligent reconstruction method have the advantages of convenient installation, no interference with the wind tunnel wind field, and the ability to measure the three-dimensional characteristics of the flow field, and can quickly and conveniently measure the three-dimensional wind field of the wind tunnel.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0009] A wind tunnel three-dimensional flow field non-interference measurement device includes an optical signal transmitting array, an optical signal receiving array, an optical signal transmitting system, and an optical signal processing system arranged on both sides of the wind tunnel.

[0010] The optical signal transmitting array is equipped with several point light sources.

[0011] The optical signal receiving array is equipped with several optical signal receivers.

[0012] The emission direction angle of each point light source can be adjusted, and the emitted light from each point light source can be directed to any one of the optical signal receivers on the optical signal receiving array.

[0013] The optical signal transmission system is connected to all point light sources, and can control the start-up timing, the direction of emitted light, and record the start-up time of each point light source.

[0014] The optical signal processing system is connected to all optical signal receivers and can record the location and time of the emitted light received by the optical signal receivers.

[0015] It also includes a wind tunnel environmental parameter measurement device, which includes a temperature measuring device and a pressure measuring device; the temperature measuring device is used to monitor the ambient temperature T inside the wind tunnel, and the pressure measuring device is used to monitor the absolute air pressure p0 inside the wind tunnel.

[0016] Both the optical signal transmitting array and the optical signal receiving array include a central encrypted region and a sparse region surrounding the central encrypted region; wherein, the spacing between point light sources or optical signal receivers in the sparse region is 1 to 3 times that of the spacing between point light sources or optical signal receivers in the central encrypted region.

[0017] The number of point light sources and optical signal receivers are equal and their positions correspond one-to-one.

[0018] A method for intelligent reconstruction of three-dimensional flow fields in a wind tunnel includes the following steps.

[0019] S1. Planning the transmission path: Each point light source arrayed on one side of the wind tunnel sequentially emits directional light to each optical signal receiver arrayed on the other side of the wind tunnel. All effective optical paths not blocked by structures inside the wind tunnel are identified, and the length of each effective optical path is calculated and sorted in ascending order to form the optical signal path sequence emitted within one cycle.

[0020] S2. Emit light signals: The point light source emits light signals into the wind tunnel one by one according to the light signal path sequence determined in S1 and records the emission time of each light signal.

[0021] S3. Receive optical signal: Record the location and reception time of the optical signal receiver, construct the path of the transmitted optical signal at the current moment and the mapping relationship between it and the actual optical signal receiver, and calculate the optical signal transmission time.

[0022] S4. Collect the ambient temperature T and absolute air pressure p0 inside the wind tunnel, and calculate the air density ρ0 when there is no wind speed.

[0023] S5. Divide the wind tunnel into several cubic grids and number them.

[0024] S6. Construct a wind tunnel three-dimensional flow field characteristic parameter analysis model based on deep learning neural network. Its inputs are the length of each effective optical path, the optical signal transmission time of each effective optical path, the mapping relationship of each effective optical path, the ambient temperature T, the absolute air pressure p0, and the air density ρ0 when there is no wind speed. The output is the wind tunnel three-dimensional flow field characteristic parameters for each grid point. Among them, the wind tunnel three-dimensional flow field characteristic parameters include density.

[0025] In S6, the three-dimensional flow field characteristic parameters of the wind tunnel also include velocity, static pressure, and dynamic pressure.

[0026] It also includes S7, which plots the wind tunnel three-dimensional flow field characteristic parameters of each grid point at the corresponding time as a visualized wind tunnel three-dimensional flow field cloud map.

[0027] It also includes S8, which constructs a prediction model for the three-dimensional flow field characteristics of the wind tunnel at future moments based on a deep learning neural network. Its input is the three-dimensional flow field characteristics of the wind tunnel at the current K time and n consecutive historical times for each grid point, and its output is the three-dimensional flow field characteristics of the wind tunnel at the future K+1 time.

[0028] In S6, the wind tunnel three-dimensional flow field characteristic parameter analysis model is trained by constructing a wind tunnel three-dimensional flow field sample library.

[0029] The wind tunnel 3D flow field sample library includes an input sample library and an output sample library.

[0030] The input sample library is obtained by repeating S2 to S4 several times according to a set periodic interval DT; wherein DT satisfies:

[0031] DT < d / U.

[0032] In the formula, d is the minimum distance between adjacent point light sources or adjacent optical signal receivers.

[0033] U represents the wind speed at the wind tunnel entrance.

[0034] The density of each grid point in the output sample library is calculated through the mapping relationship of each effective optical path.

[0035] In S5, the side length of each grid is equal and is not greater than the minimum spacing d between adjacent point light sources or adjacent optical signal receivers.

[0036] The present invention has the following beneficial effects: In this invention, the array of point light sources and the array of optical signal receivers emit optical signals in an optimized optical signal path sequence. By collecting the mapping relationship between the currently emitted optical signal path, the actual optical signal receivers receiving the optical signal, and the actual transmission time of each path, and combining the principle of light refraction, the air density of each grid point in the three-dimensional space of the wind tunnel can be calculated. Furthermore, by combining temperature and pressure measuring devices, the velocity, static pressure, and dynamic pressure of each grid point in the wind tunnel can be calculated, and a visualized three-dimensional flow field cloud map of the wind tunnel can be drawn. Attached Figure Description

[0037] Figure 1 The diagram shows a structural schematic of a wind tunnel three-dimensional flow field non-interference measurement device according to the present invention.

[0038] Figure 2 A schematic diagram of the optical signal transmitting array in this invention is shown.

[0039] Figure 3 A schematic diagram of the optical signal receiving array in this invention is shown.

[0040] Figure 4 The flowchart of an intelligent reconstruction method for a three-dimensional flow field in a wind tunnel according to the present invention is shown.

[0041] Among them are:

[0042] 1. Optical signal transmitting array; 2. Optical signal receiving array; 3. Wind tunnel; 4. Point light source; 5. Optical signal receiver; 6. Optical signal transmitting system; 7. Optical signal processing system; 8. Three-dimensional wind field post-processing system; 9. Wind tunnel environmental parameter measurement device. Detailed Implementation

[0043] The present invention will now be described in further detail with reference to the accompanying drawings and specific preferred embodiments.

[0044] In the description of this invention, it should be understood that the terms "left side," "right side," "upper part," "lower part," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. "First," "second," etc., do not indicate the importance of the components, and therefore should not be construed as a limitation of this invention. The specific dimensions used in this embodiment are only for illustrating the technical solution and do not limit the scope of protection of this invention.

[0045] like Figure 1As shown, a wind tunnel three-dimensional flow field non-interference measurement device includes an optical signal transmitting array 1, an optical signal receiving array 2, an optical signal transmitting system 6, an optical signal processing system 7, a three-dimensional wind field post-processing system 8, and a wind tunnel environmental parameter measuring device 9, which are arranged on both sides of the wind tunnel 3.

[0046] like Figure 2 As shown, the optical signal transmitting array is preferably a vertically arranged rectangle with several point light sources 4 arranged on it; the optical signal transmitting array includes a central densified region and a sparse region located around the central densified region; wherein, the spacing between the point light sources in the sparse region is 1 to 3 times that in the central densified region, that is, the spacing between the point light sources in the sparse region is consistent, and the spacing between the point light sources in the central densified region is consistent, and the central densified region and the sparse region adopt an equal proportional spacing transition, thereby improving the utilization rate of the optical signal in the central region of the flow field.

[0047] like Figure 3 As shown, the optical signal receiving array is preferably a rectangle arranged parallel to the optical signal transmitting array, and a number of optical signal receivers 5 are arranged on it; the optical signal receiving array includes a central encrypted region and a sparse region located around the central encrypted region; wherein, the spacing between the optical signal receivers in the sparse region is 1 to 3 times that in the central encrypted region, that is, the spacing between the optical signal receivers in the sparse region is consistent, the spacing between the optical signal receivers in the central encrypted region is consistent, and the central encrypted region and the sparse region adopt an equal proportional spacing transition, thereby improving the utilization rate of optical signals in the central region of the flow field.

[0048] Furthermore, the emission direction angle of each point light source can be adjusted, and the emitted light from each point light source can be directed to any one of the optical signal receivers on the optical signal receiving array.

[0049] In this embodiment, the number of point light sources and optical signal receivers is preferably equal and their positions correspond one-to-one; the arrangement of point light sources on the optical signal transmitting array and optical signal receivers on the optical signal receiving array is exactly the same, and the line connecting point light sources and optical signal receivers with the same coordinates is perpendicular to the plane where the optical signal transmitting array or the plane where the optical signal receiving array is located.

[0050] For ease of subsequent description, the point light source and the optical signal receiver will be numbered by row and column coordinates, and denoted as S(i,j) and R(i,j), respectively, representing the point light source and the optical signal receiver located in the i-th row and j-th column.

[0051] The aforementioned optical signal transmission system is connected to all point light sources, enabling control over the activation timing, emitted light direction, and recording of activation time for each point light source. The system can also count the effective paths not obstructed by the wind tunnel's internal structures, calculate the length of each effective optical path, and sort them in ascending order to determine the optical signal path sequence.

[0052] The aforementioned optical signal processing system is connected to all optical signal receivers and can record the location and time of the emitted light received by the optical signal receivers.

[0053] The wind tunnel environmental parameter measurement device includes a temperature measuring device and a pressure measuring device; the temperature measuring device is used to monitor the ambient temperature T inside the wind tunnel, and the pressure measuring device is used to monitor the absolute air pressure p0 inside the wind tunnel.

[0054] The aforementioned three-dimensional wind field post-processing system incorporates the wind tunnel three-dimensional flow field characteristic parameter analysis model, the wind tunnel three-dimensional flow field characteristic parameter prediction model for future moments, and the visualization three-dimensional image display model, as described in the subsequent intelligent reconstruction method.

[0055] like Figure 4 As shown, a method for intelligent reconstruction of a three-dimensional flow field in a wind tunnel preferably includes the following steps.

[0056] S1. Planning the launch path

[0057] S1-1. Arrange the point light sources on the optical signal transmitting array and the optical signal receivers on the optical signal receiving array, and record the coordinate positions of each point light source and each optical signal receiver.

[0058] S1-2. Detect the path obstruction between point light sources at different coordinates and optical signal receivers. Specifically, each point light source arrayed on one side of the wind tunnel emits directional light sequentially to each optical signal receiver arrayed on the other side of the wind tunnel, identifying all P effective optical paths that are not obstructed by structures inside the wind tunnel.

[0059] S1-3. Calculate the length of P effective optical paths.

[0060] S1-4. Sort the effective optical path lengths in ascending order to determine the path sequence of the optical signals emitted within one cycle.

[0061] S2. Emit light signals: The point light source emits light signals into the wind tunnel one by one according to the light signal path sequence determined in S1 and records the emission time of each light signal.

[0062] S3. Receive optical signal: Record the location and reception time of the optical signal receiver, construct the path of the transmitted optical signal at the current moment and the mapping relationship between it and the actual optical signal receiver, and calculate the optical signal transmission time.

[0063] The optical signal transmission system needs to edit the optical signal transmission sequence and transmission logic of the point light sources on the optical signal transmission array. Specifically, in each cycle, signal rays are sequentially transmitted to the initial positions of optical signal receivers at different coordinates at a very small time interval dt (preferably 1~3ns). The mapping relationship is as follows:

[0064]

[0065] Once all point light sources have transmitted light signals to the light signal receiver, the next signal transmission cycle begins.

[0066] Because the air in a wind tunnel is compressible, and air density varies at different absolute pressures, light emitted from a point light source will refract when passing through air of varying densities, resulting in... Towards The emitted light signal will actually be The receiving, or mapping relationship, is as follows:

[0067]

[0068] When light passes through media of different densities, the laws governing light emission and refraction are as follows:

[0069]

[0070] In the formula, The density of the incident air; To refract the air density; Angle of incidence; It is the angle of refraction.

[0071] The optical signal receivers on the optical signal receiving array are always turned on, and the coordinates of the optical signal receivers that actually receive the optical signal are recorded in the order of the optical signal path.

[0072] The optical signal processing system records the optical signal path sequence from the optical signal received by the optical signal receiving array and the optical signal transmitted by the optical signal transmitting system. Towards The transmitted optical signal and the optical signal receiver that actually receives the optical signal The mapping relationship g between them.

[0073] S4. Collect the ambient temperature T and absolute air pressure p0 inside the wind tunnel, and calculate the air density ρ0 when there is no wind speed according to the following formula:

[0074]

[0075] In the formula, R is the ideal gas constant, which is 8.314 J / (mol·K); M is the molar mass of the gas, which is 28.9634 g / mol for air; V is the gas volume; and m0 is the mass of the gas with volume V.

[0076] S5. Grid Division: Divide the wind tunnel into several cubic grids and number them; wherein the side length of each grid is equal and not greater than (preferably close to or equal to) the minimum spacing d between adjacent point light sources or adjacent optical signal receivers. In this embodiment, the number of grids in the X, Y, and Z directions are r, s, and t, respectively.

[0077] S6. Construct a wind tunnel three-dimensional flow field characteristic parameter analysis model based on deep learning neural network.

[0078] Inputs: length of each effective optical path, optical signal transmission time of each effective optical path, mapping relationship of each effective optical path, ambient temperature T, absolute air pressure p0, and air density ρ0 at no wind speed. Define input X at time K. K for:

[0079]

[0080] In the formula, Let K be a vector consisting of the actual transmission times of all optical signal paths at time K; where the transmission time of a single optical signal at time K is determined by the time difference between the point source and the optical signal receiver, i.e.:

[0081]

[0082] The above For the reason Towards The actual transmission time of the emitted optical signal.

[0083] L K Let be the vector consisting of the lengths of all P effective optical paths at time K, i.e.:

[0084]

[0085] Output: 3D flow field characteristic parameters of the wind tunnel at each grid point; where the 3D flow field characteristic parameters of the wind tunnel include density, velocity, static pressure and dynamic pressure, etc.

[0086] The aforementioned wind tunnel three-dimensional flow field characteristic parameter analysis model was obtained by training a wind tunnel three-dimensional flow field sample library; the wind tunnel three-dimensional flow field sample library includes an input sample library and an output sample library.

[0087] The input sample library is obtained by repeating S2 to S4 several times according to the set periodic interval DT.

[0088] like Figure 4 As shown, when no light signal is initially emitted, let time K=0 to complete one cycle from S2 to S4. That is, after one cycle, let K=K+1, until the set maximum number of cycles K is reached. max .

[0089] The above DT must meet the following requirements:

[0090] DT < d / U;

[0091] In the formula, d is the minimum distance between adjacent point light sources or adjacent optical signal receivers; U is the wind speed at the wind tunnel entrance.

[0092] Output sample library: density, velocity, static pressure and dynamic pressure of each grid point at each time step.

[0093] A. Density of each grid point at each of the above time points The solution is obtained by optimizing the mapping relationship of each effective optical path; the optimization conditions are as follows:

[0094]

[0095] The solution conditions in the above formula include the density ratio and positional relationships along the optical signal path. The boundary conditions for this optimization solution are:

[0096]

[0097]

[0098] In the formula, V i,j,k This represents the volume of the fluid domain controlled by the grid point (i,j,k); and These represent the density or air density of grid points (1,j,k) and (r,j,k), respectively.

[0099] B. Flow field velocity at each grid point at each moment mentioned above The calculation formula is:

[0100]

[0101] In the formula, This represents the grid distance that the regions with similar density distributions shift after DT time, within no more than 3 grid points on each side of grid point (i,j,k).

[0102] C. Dynamic pressure at each grid point at each moment mentioned above The expression is:

[0103]

[0104] D. Static pressure at each grid point at each moment mentioned above The expression is:

[0105]

[0106] in:

[0107]

[0108] In the formula, The absolute wind pressure value at grid point (i,j,k) is calculated based on the air density at the corresponding grid point.

[0109] S7. Plot the wind tunnel three-dimensional flow field characteristic parameters of each grid point at the corresponding time as a visualized wind tunnel three-dimensional flow field cloud map.

[0110] S8. Construct a prediction model for the three-dimensional flow field characteristic parameters of the wind tunnel at future moments based on a deep learning neural network. Its input is the three-dimensional flow field characteristic parameters S of the wind tunnel at the current K moments and n consecutive historical moments for each grid point. K S K-1 S K-2 and S K-n The output is the three-dimensional flow field characteristic parameters S of the wind tunnel at the future time K+1. K+1 The specific expression is:

[0111]

[0112] In the formula, The physical constraints of the model include fluid mass flowing in and out, and conservation of fluid momentum (in local areas of unstructured regions).

[0113] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all fall within the protection scope of the present invention.

Claims

1. A method for intelligent reconstruction of a three-dimensional flow field in a wind tunnel, characterized in that: Includes the following steps: S1. Planning the transmission path: Each point light source arrayed on one side of the wind tunnel sequentially transmits directional light to each optical signal receiver arrayed on the other side of the wind tunnel. All effective optical paths not blocked by structures inside the wind tunnel are identified, and the length of each effective optical path is calculated and sorted in ascending order as the optical signal path sequence transmitted within one cycle. S2. Emitting light signals: The point light source emits light signals into the wind tunnel one by one according to the light signal path sequence determined in S1 and records the emission time of each light signal. S3. Receive optical signal: Record the location and reception time of the optical signal receiver, construct the path of the transmitted optical signal at the current moment and the mapping relationship between it and the actual optical signal receiver, and calculate the optical signal transmission time. S4. Collect the ambient temperature inside the wind tunnel. T and absolute pressure p 0, and calculate the air density when there is no wind. ρ 0; S5. Grid division: Divide the wind tunnel into several cubic grids and number them. S6. Construct a three-dimensional flow field characteristic parameter analysis model for wind tunnels based on deep learning neural networks. The inputs are the length of each effective optical path, the optical signal transmission time of each effective optical path, the mapping relationship of each effective optical path, and the ambient temperature. T absolute pressure p Air density at 0 and no wind speed ρ 0, the output is the three-dimensional flow field characteristic parameters of the wind tunnel for each grid point; where the three-dimensional flow field characteristic parameters of the wind tunnel include density; S7. Plot the wind tunnel three-dimensional flow field characteristic parameters of each grid point at the corresponding time as a visualized wind tunnel three-dimensional flow field cloud map; S8. Construct a future-moment 3D flow field feature parameter prediction model for a wind tunnel based on a deep learning neural network. Its input is the current value of each grid point. K Time and n The wind tunnel's three-dimensional flow field characteristic parameters at consecutive historical moments are output as future data. K Characteristic parameters of the three-dimensional flow field in the wind tunnel at time +1.

2. The intelligent reconstruction method for the three-dimensional flow field of a wind tunnel according to claim 1, characterized in that: In S6, the three-dimensional flow field characteristic parameters of the wind tunnel also include velocity, static pressure, and dynamic pressure.

3. The intelligent reconstruction method for the three-dimensional flow field of a wind tunnel according to claim 1, characterized in that: In S6, the wind tunnel three-dimensional flow field characteristic parameter analysis model is trained by constructing a wind tunnel three-dimensional flow field sample library; The wind tunnel 3D flow field sample library includes an input sample library and an output sample library; The input sample library is processed according to a set periodic interval. DT Repeat steps S2 to S4 several times to obtain the result; among them, DT satisfy: DT ＜ d / U ； In the formula, d The minimum spacing between adjacent point light sources or adjacent optical signal receivers; U The wind speed at the wind tunnel entrance; The density of each grid point in the output sample library is calculated through the mapping relationship of each effective optical path.

4. The intelligent reconstruction method for the three-dimensional flow field of a wind tunnel according to claim 1, characterized in that: In S5, the side length of each grid is equal and is not greater than the minimum spacing between adjacent point light sources or adjacent optical signal receivers. d .

5. A wind tunnel three-dimensional flow field non-interference measurement device, based on the intelligent reconstruction method of the wind tunnel three-dimensional flow field according to any one of claims 1-4, characterized in that: It includes optical signal transmitting arrays, optical signal receiving arrays, optical signal transmitting systems, and optical signal processing systems deployed on both sides of the wind tunnel; The optical signal transmitting array is equipped with several point light sources; The optical signal receiving array is equipped with several optical signal receivers; The emission direction angle of each point light source can be adjusted, and the emitted light from each point light source can be directed to any optical signal receiver on the optical signal receiving array. The optical signal transmission system is connected to all point light sources, and can control the start-up timing, the direction of emitted light, and record the start-up time of each point light source; The optical signal processing system is connected to all optical signal receivers and can record the location and time of the emitted light received by the optical signal receivers.

6. The wind tunnel three-dimensional flow field non-interference measurement device according to claim 5, characterized in that: It also includes a wind tunnel environmental parameter measurement device, which comprises a temperature measuring device and a pressure measuring device; the temperature measuring device is used to monitor the ambient temperature inside the wind tunnel. T The pressure measuring device is used to monitor the absolute air pressure inside the wind tunnel. p 0.

7. The wind tunnel three-dimensional flow field non-interference measurement device according to claim 5, characterized in that: Both the optical signal transmitting array and the optical signal receiving array include a central encrypted region and a sparse region surrounding the central encrypted region; wherein, the spacing between point light sources or optical signal receivers in the sparse region is 1 to 3 times that of the spacing between point light sources or optical signal receivers in the central encrypted region.

8. The wind tunnel three-dimensional flow field non-interference measurement device according to claim 5, characterized in that: The number of point light sources and optical signal receivers are equal and their positions correspond one-to-one.

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