Non-uniform sampling flow field period reconstruction method

Abstract

The invention discloses a non-uniform sampling flow field period reconstruction method, and relates to the technical field of fluid mechanics, and the method comprises the following steps: 1, collecting a plurality of disordered flow field snapshots, carrying out the gray processing of the flow field snapshots, obtaining an initial sample, and constructing an initial sample set; 2, calculating a gray average value of each pixel point in all initial samples according to the initial sample set to obtain a sample average value; subtracting the sample mean value from each initial sample to obtain fluctuation samples, and constructing a fluctuation sample set; 3, performing eigen orthogonal decomposition according to the fluctuation sample set to obtain a main-order flow state; and 4, screening and sorting the flow field snapshots according to the modals in the main-order flow state, and reconstructing the dynamic flow field. According to the method, the vortex structure evolution process in the hole sound phenomenon can be extracted and reconstructed, and the periodic flow field is constructed.

Description

Technical Field

[0001] The present invention relates to the technical field of fluid mechanics, and more particularly to a method for periodic reconstruction of a non-uniform sampling flow field. Background Art

[0002] Hole-sound phenomenon is a phenomenon in which a jet is ejected from a nozzle and hits another baffle with a hole, accompanied by a sound of a specific frequency. Hole-sound phenomenon is ubiquitous, such as the low-frequency pressure pulsation phenomenon commonly seen in open wind tunnels, kettle whistles, and bird calls. In the field of fluid mechanics, hole-sound phenomenon is a ubiquitous flow phenomenon, and its complex and changeable vortex structure evolution has always been a hot topic and difficulty in research. In particular, the hole-sound phenomenon caused by circular nozzles usually presents a single-frequency sound feature, while other shapes of nozzles are often accompanied by more diverse characteristic frequencies, which further reveals the complexity of the shear layer vortex dynamics information. However, the current traditional research methods have significant limitations in accurately capturing and reconstructing the vortex structure in the hole-sound phenomenon, which seriously hinders the in-depth exploration of its dynamic characteristics.

[0003] Specifically, the main existing research methods include simulation calculation and experimental measurement. Although simulation calculation can simulate complex flow field changes in theory, the high-precision grid division and huge computing resource requirements lead to high computing costs, which greatly limits the practical application of this method. On the other hand, although experimental measurement can directly obtain flow field data, the amount of data generated is huge and difficult to process and store in real time, which affects the efficiency and accuracy of flow field analysis.

[0004] In addition, visualization methods also face many challenges. For example, although the method of using thick smoke accompanied by jet ejection can show the overall characteristics of the flow field, it is difficult to clearly reveal the details and evolution of the vortex structure. Although the PIV (particle image velocimetry) experiment has high resolution and accuracy, its window of view is limited and it is difficult to capture complete flow field information. At the same time, PIV equipment is expensive and has harsh operating conditions. It requires a suitable smoke particle concentration and sufficient laser brightness, which further increases the cost and complexity of the experiment.

[0005] Although the method of using smoke lines to display flow has been applied in fluid mechanics research, it is impossible to continuously capture the instantaneous flow field due to the limitations of camera shutter speed and the need to apply paraffin to produce smoke, making it difficult to reproduce the periodic characteristics of the flow field.

[0006] Therefore, how to achieve periodic reconstruction of the flow field while displaying structural details and the flow process while reducing computing power requirements and costs is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0007] In view of this, the present invention provides a method for periodic reconstruction of non-uniform sampling flow fields, which is used to visualize and study periodic flow fields, and to extract and reconstruct the evolution process of vortex structures in hole-sound phenomena.

[0008] In order to achieve the above object, the present invention adopts the following technical solution:

[0009] A method for periodic reconstruction of a non-uniform sampling flow field comprises the following steps:

[0010] Step 1: Collect several disordered flow field snapshots, perform grayscale processing on each flow field snapshot to obtain initial samples, and construct an initial sample set;

[0011] Step 2: Calculate the grayscale mean of each pixel in all initial samples according to the initial sample set to obtain the sample mean; subtract the sample mean from each initial sample to obtain the fluctuation sample and construct the fluctuation sample set;

[0012] Step 3: Perform intrinsic orthogonal decomposition based on the wave sample set to obtain the main-order flow state;

[0013] Step 4: Filter and sort the flow field snapshots according to the modes in the main-order flow state to reconstruct the dynamic flow field.

[0014] Preferably, step 1 also includes denoising and enhancing the flow field snapshot, and performing fluctuation sample filtering and data organization on the grayscale snapshot obtained after the grayscale processing.

[0015] Preferably, the sample mean in step 2 is and volatility sample set Respectively expressed as:

[0016]

[0017] Where P = {P 1 ,P 2 ,P 3 ,...,P n} represents the initial sample set; n represents the number of flow field snapshots; represents the sample mean; Represents a set of fluctuation samples.

[0018] Preferably, the specific process of step 3 is:

[0019] Step 31: Construct an autocorrelation matrix based on the fluctuation sample set, and perform eigenvalue decomposition on the autocorrelation matrix, which is expressed as;

[0020]

[0021] Ra i =λ i a i

[0022] Where R represents the autocorrelation matrix; a i Denotes the eigenvalue λ i The corresponding eigenvector;

[0023] Step 32: According to the eigenvalue λ i and the corresponding eigenvector a i , and the wave sample set is subjected to intrinsic orthogonal decomposition to obtain the main-order flow state, including the spatial mode and time mode of intrinsic orthogonal decomposition, and the expression is:

[0024]

[0025] Among them, ψ i represents the spatial modes of the eigenorthogonal decomposition, represents the time mode of the eigenorthogonal decomposition, p res is the residual, M is the number of selected reference modes, and N is the fluctuation sample set The number of samples in .

[0026] Preferably, the specific process of step 4 is:

[0027] Step 41: selecting a pair of low-order flow modes of the vortex structure with alternating changes in the flow direction phase as specific modes according to the main-order flow state; the main-order flow state includes 1-12 order modes, and since the 5th and 6th order modes constitute a time-forward and backward change of a flow in space, that is, they have alternating changes in the flow direction phase, the 5th and 6th order modes are selected as specific modes;

[0028] Step 42: Project each flow field snapshot onto a specific mode, construct the coordinate points of the flow field snapshot on the complex plane, and obtain the complex plane mode and phase of the snapshot;

[0029] Step 43: Filter out valid snapshots according to the snapshot complex plane modulus and a preset modulus threshold, and sort the valid snapshots according to their phase relationships to obtain a dynamic flow field.

[0030] Preferably, the 5th and 6th order modes are selected as specific modes. (a 5 , a 6 ) represents the coordinate point of the flow field snapshot in the complex plane. When it is greater than the modulus threshold, the flow field snapshot corresponding to the complex plane modulus of the snapshot is a valid snapshot; the modulus threshold can be taken as 0.5.

[0031] The technical effect of the above technical solution is that, within the first 12 modes with larger energy, two modes that can constitute a flow change before and after time are selected in space, and the flow field snapshots are sorted according to the energy size and phase relationship of each flow field snapshot after the projection of these two modes. The original flow field snapshot index does not have time information. After sorting, all valid snapshots can be arranged into a cycle according to the phase order of the vortex travel, which forms a time reordering.

[0032] Preferably, step 1 also includes denoising and enhancing the flow field snapshot, and constructing an initial sample set for the grayscale snapshot obtained after grayscale processing. Denoising and enhancing processing is to eliminate noise points in the image through Gaussian filtering, thereby identifying the noise area in the image and smoothing it, retaining the main details and structure of the image, and reducing the granular noise points generated in the image taken in a low-light environment; by analyzing the overall exposure of the image, adjusting the brightness, contrast and sharpening of the image, making the differences between different areas more obvious, improving the clarity of the photo, and increasing the layering of the photo.

[0033] Preferably, in the process of constructing the fluctuation sample set in step 2, fluctuation sample filtering is achieved by subtracting the sample mean from the initial sample.

[0034] Through the above technical solutions, it can be known that compared with the prior art, the present invention discloses a method for periodic reconstruction of non-uniform sampling flow fields. First, the flow field snapshots are preprocessed using image enhancement technology based on the characteristics of the flow field snapshots. Secondly, the smoke line snapshot group is analyzed using intrinsic orthogonal decomposition (POD) to efficiently decompose the flow field characteristics to extract the main vortex characteristic structure of the shear layer. Then, the snapshots are sorted in the entire periodic time axis by using adjacent main modes, and the snapshot groups can be distributed in the time axis of the same period, thereby realizing the periodic reconstruction of the flow field. In this way, the flow characteristics can be captured and analyzed more accurately, and the computational efficiency can be significantly improved and the cost can be reduced. The flow field periodic reconstruction method of the present invention is mainly used in fluid mechanics research, especially for smoke line visualization research with periodic vortex motion evolution such as the hole sound phenomenon. It can extract and reconstruct the main order unstable behaviors that are independent of each other in the hole sound phenomenon, and then reveal the evolution process of the vortex structure.

[0035] The method of the present invention not only overcomes the limitations of traditional research methods in capturing and reconstructing vortex structures, but also has the advantages of low cost and easy implementation. At the same time, through the combination of clear smoke line flow display and POD technology, it provides an intuitive visualization method, allowing researchers to clearly observe the dynamic evolution of vortex structures, and provides a powerful tool for in-depth research on the evolution of vortex structures in the hole-sound phenomenon, which helps to promote the further development of fluid mechanics research. The universality of the method of the present invention is wide, not only suitable for the study of hole-sound phenomena, but also can be applied to various flow field periodic reconstruction problems in the field of fluid mechanics, successfully solving the problem of difficult to accurately capture and reconstruct vortex structures in traditional research, and overcoming the high cost of use brought about by traditional visualization technology, such as particle image velocimetry technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0037] Figure 1 A flow chart of a method for periodic reconstruction of a non-uniform sampling flow field provided by the present invention;

[0038] Figure 2 A schematic diagram of a snapshot of the original flow field provided by the present invention;

[0039] Figure 3 A schematic diagram of the upper shear layer flipping after the original flow field snapshot provided by the present invention is grayed out;

[0040] Figure 4 A schematic diagram of the lower shear layer after the original flow field snapshot provided by the present invention is grayed out;

[0041] Figure 5 A schematic diagram of a 12th-order mode of a flow field snapshot provided by the present invention;

[0042] Figure 6 This is a schematic diagram of the change in argument and dynamic flow field provided by the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] The embodiment of the present invention discloses a method for periodic reconstruction of a non-uniform sampling flow field, comprising the following steps:

[0045] S1: Collect a number of disordered flow field snapshots, perform grayscale processing on each flow field snapshot to obtain an initial sample, and construct an initial sample set;

[0046] The flow field snapshots collected by the smoke line technology are non-uniform in time. At the same time, the flow field snapshots need to be preprocessed to highlight the flow field characteristics. The flow field snapshots are gray-scaled to obtain the initial sample set P = {P 1 ,P 2 ,P 3 ,...,P n};

[0047] S2: The grayscale mean of each pixel in all initial samples is used to obtain the sample mean; the sample mean is subtracted from each initial sample to obtain the fluctuation sample, and a fluctuation sample set is constructed after eliminating the mainstream brightness and darkness relationship;

[0048] S3: Perform intrinsic orthogonal decomposition on the extracted flow field snapshot according to the wave sample set to obtain the main-order flow state;

[0049] S4: Filter and sort the flow field snapshots according to the modes in the main-order flow state to reconstruct the dynamic flow field.

[0050] Furthermore, S1 also includes denoising and enhancing processing of the flow field snapshots, and S2 performs fluctuation sample filtering and data organization on the grayscale snapshots obtained after grayscale processing to construct a fluctuation sample set. The denoising and enhancing processing is to eliminate the noise of the image through Gaussian filtering, thereby identifying the noise area in the image and smoothing it, retaining the main details and structure of the image, and reducing the granular noise generated in the image taken in a low-light environment; by analyzing the overall exposure of the image, adjusting the brightness, contrast and sharpening of the image, the difference between different areas is more obvious, improving the clarity of the photo and increasing the layering of the photo. The initial sample is subtracted from the sample mean to achieve fluctuation sample filtering, and then data organization is performed, and the pixel matrices of all flow field snapshots are transformed in dimension and combined into a large matrix. Specifically, assuming that the grayscale information of each snapshot is stored in a pixel matrix of 2×3, and there are 5 snapshots in total, firstly, the pixel matrix of each snapshot 2×3 is converted into a column matrix of 6×1, and then the information of the 5 snapshots is arranged together to become a large matrix of 6×5. Such a large matrix contains all the information of the entire sample space at the same time.

[0051] Furthermore, the sample mean and volatility sample set Respectively expressed as:

[0052]

[0053] Where P = {P 1 ,P 2 ,P 3 ,...,P n} represents the initial sample set; n represents the number of flow field snapshots.

[0054] Furthermore, the process of performing intrinsic orthogonal decomposition to reconstruct the fluctuation sample space is:

[0055] S31: Construct an autocorrelation matrix based on the fluctuation sample set, and perform eigenvalue decomposition on the autocorrelation matrix, which is expressed as;

[0056]

[0057] Ra i =λ i a i

[0058] Where R represents the autocorrelation matrix; a i Denotes the eigenvalue λ i The corresponding eigenvector;

[0059] S32: According to the eigenvalue λ i and the corresponding eigenvector a i , and the wave sample set are subjected to proper orthogonal decomposition (POD) to obtain the main-order flow state, including the spatial mode and time mode of the proper orthogonal decomposition, which is expressed as:

[0060]

[0061] Among them, ψ i represents the spatial mode of POD, represents the time mode of POD, p res is the residual, and M is the number of selected reference modes.

[0062] Furthermore, the process of reconstructing the dynamic flow field is:

[0063] S41: According to the result of POD decomposition, i.e., the main-order flow state, a pair of low-order flow modes with alternating changes in the flow direction phase of the vortex structure are selected as specific modes, which are the criteria for judging the appearance and propagation of the vortex structure; two-order modes that can constitute a flow change before and after time in space are selected;

[0064] S42: Projecting each flow field snapshot onto a specific mode, constructing coordinate points of the flow field snapshot on a complex plane, and obtaining a complex plane mode of the snapshot;

[0065] S43: Filter out valid snapshots based on the snapshot complex plane modulus and the preset modulus threshold, and arrange them into a periodic change according to the phase order of the vortex according to the energy size and phase relationship of the specific mode in the valid snapshot to obtain the dynamic flow field. By analyzing the size of the snapshot modulus on the complex plane, stable large-scale vortex structures can be identified, and valid snapshots can be extracted. The dynamic flow field can be reconstructed according to the index and order of the valid snapshots, and the periodic changes of the dynamic flow field can be observed.

[0066] In a specific embodiment, a method for periodic reconstruction of a non-uniform sampling flow field, such as Figure 1 As shown, the following steps are included:

[0067] S1: Collect flow field snapshots and construct an initial sample set; arrange a smoke line system, use the smoke line technology to capture the flow behavior of the jet impacting the baffle with holes, obtain a flow field snapshot containing a vortex structure, and construct an initial sample set;

[0068] The current camera parameters are: shutter speed 1 / 4000, sensitivity 5.6, ISO 200. After adjusting the camera window to cover the key area of ​​the flow field, capture the flow field snapshot without stopping the wind to form the initial sample set P = {P 1 ,P 2 ,P 3 ,...,P n};

[0069] S2: Processing the flow field snapshots in the initial sample set;

[0070] like Figure 2 The left figure in the middle shows the instantaneous image of the flow field under the hole sound effect, where the nozzle is located on the left side of the figure, and a perforated baffle is configured on the right side. The fluid flows from left to right. In this image, the vortex structure formed inside the jet shear layer and its collision with the baffle can be clearly identified. Such vortex dynamic characteristics are the main factors that induce specific audio sounds;

[0071] The first step in processing the flow field snapshot is to extract the effective fluid area between the nozzle and the baffle in the flow field snapshot, while excluding the non-target smoke area at the edge. Secondly, the flow field snapshot is noise-removed, the area near the nozzle is supplemented with light, and the area near the baffle is dimmed to enhance the contrast and sharpness of the snapshot to enhance the clarity of the vortex structure. Since the flow field can be considered to follow the same vortex dynamics law, such as Figure 3 The upper shear layer is flipped as shown, Figure 4 The lower shear layers shown together constitute the sample set {p u ,p d}=P,p u represents the upper shear layer flip sample, p dRepresents the lower shear layer sample, so two samples are obtained after processing a flow field snapshot to obtain the entire vortex structure; then the three RGB channels of the color flow field snapshot are converted into grayscale values ​​GRAY based on the weights of the human eye's sensitivity to different color channels. The formula is defined as:

[0072] P Gray =0.299×P R +0.587×P G +0.114×P B ;

[0073] S3: Calculate the average value of the sample based on the sample set In this way, the mean of the image is obtained, and then for each sample, the fluctuation sample set is constructed by subtracting the mean

[0074] The average value of the samples reveals the main concentrated areas of convective activity and reduces the interference caused by the brightness changes of smoke lines in the mainstream area. After the mean is removed, the irrelevant information areas in the fluctuation samples show a decrease in grayscale value, while the brightness difference between the mainstream and the shear layer area is still significant, and the large-scale vortex structure in the shear layer becomes more distinct.

[0075] When collating the data set, each snapshot is first converted into a two-dimensional matrix based on its pixel grayscale information, and then these two-dimensional matrices are further converted into one-dimensional matrices; then, according to the index order of the snapshots, these one-dimensional matrices are orderly arranged and combined into a new matrix Finally, the matrix This constitutes the fluctuation sample set after all fluctuation samples have gone through the above processing flow;

[0076] S4: spatiotemporal feature analysis;

[0077] Constructing the volatility sample space The autocorrelation coefficient matrix And perform eigenvalue decomposition Ra i =λ i a i , a i Represents and eigenvalue λ i The corresponding eigenvector;

[0078] The modes using proper orthogonal decomposition (POD) can be expressed as:

[0079]

[0080] In the formula, ψ i represents the spatial mode of POD, is the time mode of POD, p resrepresents the residual, M is the number of selected reference modes, and N is the fluctuation sample set The number of samples in ;

[0081] S5: Filter out valid snapshots from the flow field snapshots according to the intrinsic orthogonal decomposition results, and reconstruct the dynamic flow field according to the index and order of the valid snapshots;

[0082] S51: Figure 5 The first 12 modes of the flow field snapshot are displayed, among which the 1st and 2nd modes (Mode1, Mode2) mainly depict the changes in light and shadow during the shooting process, which can be regarded as the main range of the flow. The 3rd mode (Mode3) reflects that the instability of the flow is mainly concentrated in the shear layer and partially invades the core flow field. The 4th to 12th modes (Mode4-Mode12) highlight the main positions of various large eddy structures in the jet shear layer, while higher-order modes provide more detailed information of the flow. It is worth noting that the 5th and 6th modes (Mode5, Mode6) mainly describe the alternating phase changes of the vortex structure in the flow direction, forming a pair of low-order flow patterns, which can be used as an important sign to judge the appearance and propagation of large eddy structures. Therefore, the dynamic flow field is reconstructed based on the 5th and 6th modes to capture the generation, evolution and dissipation process of the vortex structure, so as to have a deeper understanding of the dynamic characteristics of the hole sound phenomenon.

[0083] S52: sample sorting;

[0084] By projecting each flow field snapshot onto the fifth and sixth order modes, the coordinate point (a 5 (j),a 6 (j)), j represents the jth flow field snapshot, and the complex plane model of the snapshot is calculated according to the coordinate point The projection coefficients (i.e., eigenvectors) of each mode projection of all flow field snapshots are expressed as a i =[a i (1),a i (2),...a i (n)], i represents the i-th mode, and n represents the number of flow field snapshots;

[0085] A 5 (j) is abbreviated as a 5 Represents the feature vector of the flow field snapshot projected onto the fifth-order mode, 6 (j) is abbreviated as a 6 It represents the feature vector of the flow field snapshot projected to the sixth-order mode. It is screened according to the size of the snapshot complex plane modulus, and invalid samples are eliminated to screen out the current The flow field snapshots that are larger than the modulus threshold are valid snapshots; when the modulus value exceeds a certain threshold, the snapshot is considered to have captured a stable large-scale vortex structure and is considered a valid sample. The valid snapshots and their indices are represented on the complex plane.

[0086] Snapshot complex plane (a 5 +ia 6 ) represents the phase, and the valid snapshots are sorted according to the phase relationship; starting from the positive semi-axis of the complex plane y-axis, the valid snapshots are sorted in a counterclockwise direction according to the argument of each valid snapshot, and an orderly periodic motion of the flow field can be obtained to construct a dynamic flow field; the argument θ = arctan(a 6 / a 5 );

[0087] The corresponding change of the argument angle and the dynamic flow field are as follows: Figure 6 As shown, Figure 6 The left side of (a)-(f) shows the change of the angle of argument, and the horizontal axis represents a 5 / λ 5 , the vertical axis represents a 6 / λ 6 , the horizontal and vertical axes are the scale representations after the normalized eigenvector, and the right side is the dynamic flow field change diagram; Figure 6 Where (a) is t 1 The main vortex structure a can be clearly observed. 1 , and the small vortex structure b upstream 1 Beginning to take shape; Figure 6 (b) is t 2 Time changes, main vortex structure a 1 Go to a 2 Position, close to the downstream baffle, at this time b 1 The scale growth is b 2 , initial vortex structure c 2 Begins to form; Figure 6 Where (c) is t 3 Constantly changing, 2 、c 2 The scale continues to grow and move downstream to b 3 、c 3 , a 3 It has collided with the baffle plate at the edge of the orifice and deformed; Figure 6 (d) is t 4 Time changes, affected by upstream 4 Squeeze, a 4 The deformation is more serious and deviates downward along the baffle, while releasing energy to affect the flow field in the core area; Figure 6 Where (e) is t 5 Time-varying, large eddy structure b 5It grows and moves to the middle and rear section of the jet length, which is the moment when the vortex structure invades the core flow field of the test section most seriously; Figure 6 Where (f) is t 6 Constant changes, preamble structure b 6 Energy dissipation, vortex c 6 Move downstream and grow, corresponding to t 1 Time vortex structure a 1 ; So far, a cycle of flow field movement is completed, and the vortex structure collides with the baffle twice and dissipates. The main vortex structure and the upstream vortex structure refer to the ordered flow field periodic structure obtained after the original only clipped flow field snapshot is reordered according to the calculation results. Therefore, several representative time nodes are selected to divide the entire cycle, and the motion trajectory of the vortex at these time nodes is analyzed to obtain the number of times the vortex between the nozzle and the baffle appears and breaks in one cycle, that is, the evolution process of the vortex. This process corresponds to the natural frequency of the generated noise. From the above structural analysis, it can be seen that in the flow field structure collected in the present invention, there are two vortex structures born and broken in one cycle, corresponding to the second-order modal noise of the hole sound.

[0088] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0089] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for periodic reconstruction of a non-uniform sampling flow field, characterized in that: The following steps are involved: Step 1: Collect several disordered flow field snapshots, perform grayscale processing on each flow field snapshot to obtain initial samples, and construct an initial sample set; Step 2: Calculate the grayscale mean of each pixel in all initial samples according to the initial sample set to obtain the sample mean; Subtract the sample mean from each initial sample to obtain the fluctuation sample and construct the fluctuation sample set; Step 3: Perform intrinsic orthogonal decomposition based on the wave sample set to obtain the main-order flow state; Step 4: Filter and sort the flow field snapshots according to the modes in the main-order flow state to reconstruct the dynamic flow field.

2. A method for periodic reconstruction of a non-uniform sampling flow field according to claim 1, characterized in that: The sample mean and fluctuation sample set in step 2 are expressed as: Where P = {P1, P2, P3, ..., P n } represents the initial sample set; represents the sample mean; n represents the number of flow field snapshots; Represents a set of fluctuation samples.

3. A method for periodic reconstruction of a non-uniform sampling flow field according to claim 1, characterized in that: The specific process of step 3 is: Step 31: Construct an autocorrelation matrix based on the fluctuation sample set, and perform eigenvalue decomposition on the autocorrelation matrix, which is expressed as; Ra i =λ i a i Where R represents the autocorrelation matrix; a i Denotes the eigenvalue λ i The corresponding eigenvector; Step 32: According to the eigenvalue λ i and the corresponding eigenvector a i , and the wave sample set is subjected to intrinsic orthogonal decomposition to obtain the main-order flow state, including the spatial mode and time mode of intrinsic orthogonal decomposition, and the expression is: Among them, ψ i represents the spatial modes of the eigenorthogonal decomposition, represents the time mode of the eigenorthogonal decomposition, p res is the residual, M is the number of selected reference modes, and N is the fluctuation sample set The number of samples in .

4. The method for periodic reconstruction of a non-uniform sampling flow field according to claim 1, characterized in that: The specific process of step 4 is: Step 41: selecting a pair of low-order flow modes of the vortex structure with alternating changes in the flow direction phase according to the main-order flow state as specific modes; Step 42: Project each flow field snapshot onto a specific mode, construct the coordinate points of the flow field snapshot on the complex plane, and obtain the complex plane mode and phase of the snapshot; Step 43: Filter out valid snapshots according to the snapshot complex plane modulus and a preset modulus threshold, and sort the valid snapshots according to their phase relationships to obtain a dynamic flow field.

5. A method for periodic reconstruction of a non-uniform sampling flow field according to claim 4, characterized in that: Select the 5th and 6th order modes as specific modes. (a5, a6) represents the coordinate point of the flow field snapshot in the complex plane. When it is greater than the modulus threshold, the flow field snapshot corresponding to the complex plane modulus of the snapshot is a valid snapshot.

6. A method for periodic reconstruction of a non-uniform sampling flow field according to claim 1, characterized in that: Step 1 also includes denoising and enhancing the flow field snapshot, and grayscale processing of the denoised and enhanced flow field snapshot; in step 2, the fluctuation sample data is organized to construct a fluctuation sample set.

Images