A method for analyzing pulsating flow field of underwater vehicle and sound source correlation

Abstract

The application belongs to the field of fan aerodynamic performance, and discloses a pulsating flow field and sound source correlation analysis method of an underwater vehicle, wherein a SUBOFF standard model is based on a simulation environment to obtain flow field simulation information, a pulsating flow field velocity flow field cloud picture and a streamline distribution picture corresponding to a longitudinal section of the underwater vehicle are drawn based on the flow field simulation information, a third generation vortex identification method Liutex is used to calculate flow field vortex structure, and visual flow field vortex structure is calculated; according to vortex field different modes and different scale vortex results and pulsating pressure measuring point spectrum results in step six, a correlation relationship between flow field different scale vortices and sound source high frequency and low frequency is established according to a mode frequency corresponding relationship, and a correlation rule is summarized.

Description

Technical Field

[0001] This invention relates to the field of wind turbine aerodynamic performance, and more specifically, to a method for analyzing the correlation between pulsating flow field and sound source of an underwater vehicle. Background Technology

[0002] When an underwater vehicle travels underwater, a boundary layer forms on its surface. The boundary layer is usually thin. As the boundary layer gradually develops, it undergoes a complex process from laminar boundary layer to transition to turbulent boundary layer. During this process, flow separation occurs in the flow field, accompanied by the generation, evolution and stripping of complex vortex structures. The flow field usually exhibits high turbulence and strong pulsating flow characteristics, and has strong spatiotemporal dynamic change characteristics.

[0003] The pulsating pressure on the surface of an underwater vehicle is an important manifestation of the pulsating nature of the flow field. As the excitation source of underwater noise, how to effectively analyze the pulsating pressure on the surface of an underwater vehicle is a key issue that must be solved in the prediction of hydrodynamic noise of the vehicle. It is of great significance for understanding the flow characteristics of underwater vehicles and predicting flow-induced noise.

[0004] Defects and shortcomings of existing technology:

[0005] Current post-processing analysis methods for pulsating flow fields of underwater vehicles mainly analyze the flow field and sound source separately, and have not yet formed a comprehensive analysis method that correlates the flow field and sound source, and lack a comprehensive analysis method for their correlation law.

[0006] In view of this, the present invention provides a method for analyzing the correlation between the pulsating flow field and sound source of an underwater vehicle. Summary of the Invention

[0007] To overcome the problems in the existing technology, this invention proposes a method for analyzing the correlation between the pulsating flow field and sound source of an underwater vehicle. This comprehensive post-processing method, which correlates the pulsating flow field around the underwater vehicle with the characteristics of the sound source, helps to understand the flow mechanism of the pulsating flow field, systematically extract and analyze the characteristics of the sound source, and study the correlation between the pulsating flow field and the sound source. As a basis for conducting flow-induced noise prediction, it can be applied to the study of numerical simulation engineering problems of underwater vehicles.

[0008] To address the aforementioned problems, the present invention provides the following technical solution: Firstly, the present invention provides a method for analyzing the correlation between the pulsating flow field and sound sources of an underwater vehicle, comprising the following steps:

[0009] Step 1: Obtain flow field simulation information based on the SUBOFF standard model in the simulation environment;

[0010] Step 2: Draw a velocity flow field cloud map of the pulsating flow field based on the flow field simulation information;

[0011] Step 3: Based on the numerical simulation results of the flow field, calculate and determine the streamlines of the flow field according to the definition of streamlines, and draw the streamline distribution diagram corresponding to the longitudinal section of the underwater vehicle.

[0012] Step 4: Based on the velocity contour maps and streamline diagrams obtained in Steps 2 and 3, and the resulting flow field diagrams showing the distribution of velocity magnitude and direction, the third-generation vortex identification method, Liutex, is used to calculate the vortex structure of the flow field, calculating the vortex distribution at each coordinate position. extract isosurfaces, select Isosurface visualization of vortex structure in flow field;

[0013] Step 5: Use dynamic mode decomposition technology to decompose... The field yields different modes and scales of vorticity field.

[0014] Step 6: Based on the flow field simulation information corresponding to continuous time points, extract the pulsating pressure data at specific measuring points of the underwater vehicle, and use plotting software to draw the time-history curve of the pulsating pressure data at the pressure measuring points;

[0015] Step 7: The spectrum of the pulsating pressure data at specific measuring points on the underwater vehicle is plotted using spectral analysis.

[0016] Step 8: Plot the 1 / 3 octave spectrum of the pulsating pressure data at the pressure measurement point, and perform Fourier transform or power spectral density estimation on the pulsating pressure signal using signal analysis software to obtain the signal spectrum;

[0017] Step 9: Plot the wavenumber-frequency spectrum of the pulsating pressure data from the pressure measurement array;

[0018] Step 10: Based on the results of different modes and different scales of vorticity field obtained by the dynamic mode decomposition technique in Step 5, and the spectrum results of the pulsating pressure measurement points in Step 6, establish the correlation between vortices of different scales in the flow field and the high and low frequencies of the sound source according to the modal frequency correspondence, and summarize the correlation law.

[0019] As a preferred embodiment of the present invention, the flow field simulation information includes the velocity and pressure information of the SUBOFF standard model at various coordinate positions in the flow field environment at the current moment.

[0020] As a preferred embodiment of the present invention, the vortex structure of the flow field shows that the flow around the underwater vehicle has gone through laminar flow, transition and turbulent boundary layer flow states. Most of the surface of the underwater vehicle is a turbulent boundary layer, and a typical hairpin-shaped vortex structure can be observed, reflecting the complex pulsating flow field near the wall of the underwater vehicle.

[0021] As a preferred embodiment of the present invention, the logic for obtaining the visualized flow field vortex structure is as follows:

[0022] In the initial xyz coordinate system, the flow field simulation velocity information is obtained based on the simulation environment. The velocity gradient tensor ▽v and its eigenvalues ​​are calculated. When the velocity gradient tensor has two complex conjugate eigenvalues, it indicates that the instantaneous streamline is circular or spiral. The velocity gradient tensor is marked as a vortex structure.

[0023] The velocity gradient tensor is normalized using Liutex to obtain a vortex identification method that does not depend on the isosurface threshold. The normalized Liutex expression is as follows:

[0024]

[0025] As a preferred embodiment of the present invention, the specific decomposition logic of the dynamic mode decomposition technology is as follows:

[0026] For unsteady flow field time series, the N snapshots obtained through experiments or numerical simulations can be written as a snapshot sequence from time 1 to N, i.e., {x1, x2, x3, ..., x...} N}, where the snapshot at time i is represented as a column vector x. i And the time interval between any two snapshots is Δt;

[0027] Assuming the flow field x i+1 Through the flow field x i The linear mapping representation is:

[0028] x i+1 =Ax i ;

[0029] Where A is the system matrix of the high-dimensional flow field. If the dynamic system itself is nonlinear, then this process is a linear estimation process.

[0030] Based on the assumed linear mapping relationship, matrix A can reflect the dynamic characteristics of the system. A can be calculated from the data sequence by reducing the order.

[0031] Using flow field snapshots from time 1 to N, two snapshot matrices X = [x1, x2, x3, ..., xn] can be constructed. N-1 ] and Y = [x2, x3, x4, ..., x N Based on the assumptions in the above formula, we can conclude that:

[0032] Y = [x2, x3, x4, ..., x N ]

[0033] =[Ax1,Ax2,Ax3,...,Ax N-1] = AX;

[0034] By performing mathematical transformations on the above snapshot matrix, the dominant eigenvalues ​​and main modes are extracted. As the number of snapshots N increases, the data sequence {x1,x2,x3,...,x...} is further analyzed. N It has been able to capture the main physical characteristics.

[0035] As a preferred embodiment of the present invention, the analysis logic of the signal spectrum results is as follows:

[0036] The spectrum is divided into a series of 1 / 3 octave bands, and the width of each frequency band is 1 / 3 of the width of the previous frequency band.

[0037] Frequency bands are typically divided using a logarithmic scale, and the energy within each frequency band is calculated.

[0038] The energy within each frequency band is normalized using a logarithmic scale, and the 1 / 3 octave spectrum of the pulsating pressure data at the pressure measurement point is plotted using plotting software; thus, the spectral result of the signal is obtained.

[0039] As a preferred embodiment of the present invention, the pulsating pressure data at specific measurement point array positions of the underwater vehicle is extracted, and the pulsating pressure at each measurement point of the array is subjected to a two-dimensional Fourier transform based on signal analysis software. The result of the two-dimensional Fourier transform is frequency shifted so that the center of the two-sided spectrum is located at the origin. Finally, the wavenumber-frequency spectrum is plotted based on the calculated result using plotting software.

[0040] In a second aspect, based on the method of the first aspect, the present invention also proposes an electronic device comprising:

[0041] At least one processor; and

[0042] A memory that is communicatively connected to the at least one processor;

[0043] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, cause the electronic device to perform the underwater vehicle pulsating flow field and sound source correlation analysis method.

[0044] Thirdly, based on the method of the first aspect, the present invention also proposes a storage medium on which a computer program is stored, which, when executed by a processor, implements the aforementioned method for analyzing the correlation between the pulsating flow field and sound source of an underwater vehicle.

[0045] The technical effects and advantages of the method for analyzing the correlation between the pulsating flow field and sound source of an underwater vehicle according to the present invention are as follows:

[0046] This invention proposes an analytical method for the correlation between flow field and sound source by comprehensively analyzing the characteristics of pulsating flow field and sound source, from the perspective of the interrelation between vortex dynamic characteristics and sound source spectrum characteristics. It forms an effective and comprehensive post-processing analysis process, which is of great significance for exploring its flow mechanism and predicting flow-induced noise, and can also provide important parameter basis for the design and development of underwater vehicle equipment. Attached Figure Description

[0047] Figure 1 This is a flowchart of the underwater vehicle pulsating flow field and sound source correlation analysis method of the present invention;

[0048] Figure 2 This is a flowchart illustrating the calculation of the pulsating pressure spectrum and 1 / 3 octave band spectrum of the present invention.

[0049] Figure 3 This is a flowchart illustrating the calculation process of the flow field data of the present invention under different spatial pressures and temporal pressures.

[0050] Figure 4 This is a model diagram of the SUBOFF underwater vehicle standard model of the present invention;

[0051] Figure 5 This is a velocity contour map of the cross section of the pulsating flow field of the present invention;

[0052] Figure 6 This is a diagram showing the streamline distribution on the longitudinal section of the underwater vehicle body of the present invention;

[0053] Figure 7 This invention provides a visualization of the vortex structure of the pulsating flow field.

[0054] Figure 8 The diagram shows the vorticity field structure corresponding to different modes and scales of vortices in this invention.

[0055] Figure 9 This is a time-history curve of the pulsating pressure data of the pressure measuring point in this invention;

[0056] Figure 10 This is a spectrum diagram of the pulsating pressure data at the pressure measuring point of the present invention;

[0057] Figure 11 This is a 1 / 3 octave band spectrum of the pulsating pressure data of this invention;

[0058] Figure 12 This is a spatiotemporal distribution diagram of the sound source caused by the pulsating pressure of the present invention;

[0059] Figure 13 This is a diagram showing the correlation between the low-frequency sound source of this invention and eddies of different scales in the flow field.

[0060] Figure 14 This is a diagram showing the correlation between the high-frequency sound source of this invention and eddies of different scales in the flow field. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] The SUBOFF underwater vehicle model, as a publicly available underwater vehicle model, has been extensively studied through experimental and numerical simulations. For the SUBOFF underwater vehicle model, the flow field data obtained using the wall eddy simulation method is presented. The simulation parameters are shown in Table 1, with the SUBOFF head position taken as the origin. The SUBOFF model is attached. Figure 4 As shown. After the flow field stabilizes, numerical simulation results are obtained, from which velocity field data around the underwater vehicle are extracted.

[0063] Table 1: Geometric and Calculation Parameters of the SUBOFF Underwater Vehicle Model

[0064] Maximum diameter 0.508m Flow rate 2.837m / s Reynolds number <![CDATA[1.24×10 7 ]]>

[0065] Example 1

[0066] Please see Figure 1-3 As shown in this embodiment, a method for analyzing the correlation between the pulsating flow field and sound source of an underwater vehicle includes the following steps:

[0067] Step 1: Obtain flow field simulation information from the SUBOFF standard model based on the simulation environment; the flow field simulation information includes the velocity and pressure information of the SUBOFF standard model at each coordinate position in the flow field environment at the current moment;

[0068] It should be noted that the SUBOFF standard model can accurately reflect actual fluid dynamics behavior. Based on the actual flow field where the SUBOFF standard model is located, and all relevant fluid dynamic parameters such as fluid density, viscosity, and temperature, appropriate fluid dynamics simulation software is used to run the flow field simulation in the set simulation environment to generate flow field simulation information.

[0069] Inputting the flow field results obtained from numerical simulation, including velocity and pressure information at various locations throughout the flow field, allows observation of the flow field distribution, flow characteristics, pressure distribution, etc., to understand the performance and behavior of the SUBOFF standard model in the flow field. This is of great significance for evaluating model performance, optimizing design, and predicting fluid dynamics behavior in practical applications.

[0070] Step 2: Draw a velocity flow field cloud map of the pulsating flow field based on the flow field simulation information;

[0071] Based on the obtained numerical simulation results of the flow field, a velocity cloud map of the longitudinal section of the underwater vehicle flow field was plotted using post-processing software. The velocity was normalized to the fluid velocity at infinity, allowing the velocity field to be expressed in a dimensionless manner. Typical pulsation characteristics can be observed from the non-uniformity of the flow field velocity near the wall and in the wake, as shown in the attached figure. Figure 5 As shown;

[0072] Post-processing software includes, but is not limited to, Paraview, Tecplot, and Visit software.

[0073] Step 3: Based on the numerical simulation results of the flow field, calculate and determine the streamlines of the flow field according to the definition of streamlines, and draw the streamline distribution diagram corresponding to the longitudinal section of the underwater vehicle, such as... Figure 6 As shown; the formula for calculating the streamline definition is:

[0074] dr×u=0 (1)

[0075] Where: r is the position vector; dr is the streamline arc element; u is the velocity vector field;

[0076] It should be noted that different points on a streamline correspond to different particles in the flow field at the same moment. dr×u=0 means that the tangent direction at any point on the streamline is exactly the instantaneous velocity direction at that point, and thus the streamline distribution can be obtained.

[0077] In addition, the aforementioned longitudinal section is a section or cross-section carried out in the longitudinal direction (i.e., along its length) of the underwater vehicle, and the cross-section is located on the symmetrical plane in the middle of the underwater vehicle.

[0078] Step 4: Based on the velocity contour maps and streamline diagrams obtained in Steps 2 and 3, and the resulting flow field diagrams showing the distribution of velocity magnitude and direction, the third-generation vortex identification method, Liutex, is used to calculate the vortex structure of the flow field, calculating the vortex distribution at each coordinate position. Using this value provides a better understanding of the vortex dynamics characteristics of the flow field. Extraction The isosurfaces are then selected based on post-processing software. Isosurface visualization of vortex structure in flow field, such as Figure 7 As shown, the vortex structure of the flow field indicates that the flow around the underwater vehicle has gone through laminar, transition, and turbulent boundary layer states. Most of the surface of the underwater vehicle is a turbulent boundary layer, and a typical hairpin-shaped vortex structure can be observed, reflecting the complex pulsating flow field near the wall of the underwater vehicle.

[0079] The Liutex theory, the third-generation eddy current identification method, is introduced in detail below.

[0080] Traditional velocity gradient tensor decomposition depends on the coordinate system, not Galilean rotation invariants. Therefore, the third-generation vortex identification method, Liutex, obtains Liutex rigid rotation during the process of seeking the tensor decomposition of the Galilean invariant velocity gradient, thus establishing the Liutex vortex identification method. To find the unique decomposition of the velocity gradient tensor, the corresponding principal coordinate system must be found.

[0081] In the initial xyz coordinate system, to obtain the vortex dynamic characteristics of the flow field, the simulated velocity information of the flow field is obtained based on the simulation environment, and the velocity gradient tensor is calculated. Its eigenvalues, the velocity gradient tensor, are usually the derivatives of the velocity vector. It contains information about the change of velocity in the fluid with spatial position and describes the rate of change of the velocity field in the fluid. Having two complex convolutional characteristic roots indicates that the instantaneous streamlines are circular or spiral, i.e., a vortex structure exists.

[0082] First, determine the Z-axis. Then, rotate the original coordinate system in three dimensions so that the Z-axis of the resulting coordinate system is aligned with the Z-axis of the original coordinate system. The real eigenvectors k are parallel, and w·k > 0 is defined, with k being a unit vector:

[0083]

[0084] Having determined the Z-axis, we then determine the X and Y axes perpendicular to it. Rotating the ▽V in the above equation by an angle θ in the XY plane, we obtain:

[0085]

[0086] In the formula, Only when θ rotates to When this value is equal to the real part of the two complex eigenvalues, a unique principal coordinate system is found, and the eigenvalues ​​remain unchanged after Galilean rotation.

[0087]

[0088] Since Q and P undergo real orthogonal rotations, the eigenvalues ​​remain unchanged. and The eigenvalues ​​are the same. That is, λ. k , λ cr +iλ ci ,λ cr -iλ ci Or λ1, λ2, λ3 in (x, y, z)(X, Y, Z)(X θ ,Y θThe values ​​of Z are the same in different inertial coordinate systems. Physical rotation is entirely determined by the off-diagonal elements of the top-left two-dimensional matrix; the diagonal elements only represent stretching or compression.

[0089] Assuming the fluid rotates, make

[0090] The principal coordinate velocity gradient tensor becomes:

[0091]

[0092] vorticity w and λ k ,λ ci These are all Galilean invariants, which can be calculated in the original coordinates.

[0093]

[0094]

[0095] The size of Liutex is calculated as follows:

[0096]

[0097] In the formula w k =w·k, therefore the Liutex vector is defined as:

[0098] Liutex=r·k (9)

[0099] Based on this, the Liutex is normalized to obtain a eddy recognition method that does not depend on the isosurface threshold. The normalized Liutex expression is as follows:

[0100]

[0101] Step 5: Based on the Liutex method, a third-generation eddy covariance identification technique... The field is decomposed using dynamic mode decomposition technology based on programming software. The field yields different modes and scales of vorticity fields, as shown in the appendix. Figure 8 As shown.

[0102] The dynamic mode decomposition technique is described in detail below.

[0103] Before performing dynamic modal decomposition analysis, the unsteady flow field time series needs to be processed. N snapshots obtained through experiments or numerical simulations can be written as a snapshot sequence from time 1 to N, i.e., {x1, x2, x3, ..., x...} N}, where the snapshot at time i is represented as a column vector x. iAnd the time interval between any two snapshots is Δt. Assume the flow field x i+1 Through the flow field x i The linear mapping representation is:

[0104] x i+1 =Ax i (11)

[0105] Here, A is the system matrix of the high-dimensional flow field. If the dynamic system itself is nonlinear, then this process is a linear estimation process. Based on the assumed linear mapping relationship, matrix A can reflect the dynamic characteristics of the system. Since the dimension of A is very high, it needs to be calculated from the data sequence through a method of order reduction. Using the flow field snapshots from time 1 to N, two snapshot matrices X = [x1, x2, x3, ..., x...] can be constructed. N-1 ] and Y = [x2, x3, x4, ..., x N Based on the assumptions in the above formula, we can conclude that:

[0106] Y = [x2, x3, x4, ..., x N ]

[0107] =[Ax1,Ax2,Ax3,...,Ax N-1 ]=AX (12)

[0108] The purpose of dynamic mode decomposition (VMD) is to extract dominant eigenvalues ​​and main modes by performing mathematical transformations on the aforementioned snapshot matrix. Based on the linear dynamics assumption, VMD can be achieved through the following methods:

[0109] A friend matrix is ​​a special type of matrix where the last column contains any value, the elements above or below the main diagonal are all 1s, and the elements on the main diagonal and all other elements are zeros. As the number of snapshots N increases, the data sequence {x1,x2,x3,...,x...}... N Since the main physical characteristics have already been captured, we can further assume that after a certain number of snapshots, the flow field snapshots become linearly independent. Therefore, the last snapshot can be represented as a linear superposition of all previous flow field snapshots:

[0110] x N = c1x1 + c2x2 + c3x3 + ... + c N-1 x N-1 =Xc (13)

[0111] but:

[0112] AX = Y = XS (14)

[0113] In the above formula, matrix S is a friend matrix:

[0114]

[0115] Since the only unknown in S is the matrix c, we can find the c that minimizes the residual r to construct S:

[0116] r = x N -Xc (16)

[0117] When the residual is minimized, the eigenvalues ​​of S become approximations of the eigenvalues ​​of A. Compared to matrix A, matrix S represents the lower-dimensional form of the entire system after order reduction, and its eigenvalues ​​can represent the main eigenvalues ​​of matrix A. The eigenvalues ​​of S are called Ritz eigenvalues. The eigendecomposition of S is...

[0118] S = T -1 ΛT,Λ=diag(λ1,...,λ N-1 (17)

[0119] Where Λ is a diagonal matrix composed of the eigenvalues ​​of S, and the corresponding eigenvector is T. -1 The columns. Define the DMD mode d as a column vector of moments D, where D = XT. -1 To achieve flow field reconstruction, the Vandermonde matrix can be introduced:

[0120]

[0121] matrix It has a strong correlation with friend matrices because it can diagonalize friend matrices that have no repeated eigenvalues. Therefore It is also an accurate estimate of T, that is Therefore, the flow field snapshot can be represented as:

[0122]

[0123] Where m represents the number of modes selected for the dynamic mode decomposition technique. A snapshot of the flow field at any given time can be represented by the first m snapshots as follows:

[0124]

[0125] The growth rate g corresponding to the j-th mode j and frequency ω j Defined as:

[0126] g j =Re{lg(λ j )} / Δt (21)

[0127] ω j =Im{lg(λ j )} / Δt (22)

[0128] The programming software mentioned in step five includes Python, MATLAB, etc.

[0129] Step Six: Based on the flow field simulation information corresponding to continuous time points, extract the pulsating pressure data at specific measuring points on the underwater vehicle. Use plotting software to draw the time-history curves of the pulsating pressure data at the pressure measuring points, as shown in the attached figure. Figure 9 As shown, the time-history curve is used to display the change of a variable over time. This variable can be any quantity that changes over a period of time, such as position, velocity, temperature, pressure, etc. It is typically plotted with time on the horizontal axis and the corresponding variable value on the vertical axis. From the pulsating pressure time-history curve, it can be seen that the pressure at the wall exhibits significant fluctuations over time, constantly changing around the mean value with an amplitude of approximately 200 Pa. The frequency components are complex, reflecting the pulsating nature of the flow field obtained from the numerical simulation.

[0130] It should be noted that: continuous moments refer to moments with relatively small time intervals that can be regarded as continuously changing;

[0131] The plotting software used in step six includes MATLAB, Python, OriginLab, etc.

[0132] Step 7: At specific measuring points on the underwater vehicle, the pulsating pressure data is analyzed using spectral analysis to plot the spectrum of the pulsating pressure data at each measuring point. The spectrum refers to the frequency domain representation of the pulsating pressure signal, such as... Figure 10 As shown, the spectral analysis of pulsating pressure signals can be represented using power spectral density (PSD). Power spectral density represents the signal power per unit frequency range. For discrete signals, a commonly used method for estimating power spectral density is based on signal analysis software and uses the Discrete Fourier Transform (DFT).

[0133] Formula for calculating the pulsating pressure spectrum:

[0134]

[0135] SPL represents sound pressure level, expressed in decibels (dB). The sound pressure level is the logarithmic representation of the pulsating pressure power spectral density signal, used to quantify and compare sound intensity. PSD represents the power spectral density obtained after Fourier transforming the pulsating pressure time-history curve. P0 is the reference pressure, typically taken as 1 × 10⁻⁶. -6 Pa.

[0136] In practical applications, different window functions can be used to improve the properties of spectral estimation. A window function is a mathematical function used to process finite-length signals. By multiplying the signal by the window function, discontinuities at both ends of the signal are reduced, which helps to perform more accurate spectral analysis. The goal of a window function is to reduce the effect of spectral leakage when applying Fourier transform to a finite-length signal. Spectral leakage refers to the extra spectral components caused by multiplying the signal by the window function when the signal period is not an integer multiple of the window length. Common window functions include the Hanning window and the Hamming window.

[0137] from Figure 10 As can be seen, as the frequency increases, the magnitude of the pulsating pressure spectrum sound pressure level at this measuring point gradually decreases, exhibiting the characteristics of a plateau region in the lower frequency region, with an inflection point at 80Hz.

[0138] The signal analysis software used in step seven includes MATLAB, Python, etc.

[0139] Step 8: Plot the 1 / 3 octave spectrum of the pulsating pressure data at the pressure measurement point. Perform Fourier transform or power spectral density estimation on the pulsating pressure signal using signal analysis software to obtain the signal spectrum. Then, divide the spectrum into a series of 1 / 3 octave bands, with each band's width being 1 / 3 of the previous band's width. A logarithmic scale is typically used to divide the frequency bands. Within each band, calculate the energy. Normalize the energy within each band using a logarithmic scale, and then use plotting software to plot the 1 / 3 octave spectrum of the pulsating pressure data at the pressure measurement point. The calculation process for the pulsating pressure spectrum and 1 / 3 octave spectrum is as follows: Figure 2 As shown. The results are as follows. Figure 11 As shown, from Figure 11 As can be seen, as the frequency increases, the pulsating pressure 1 / 3 octave band spectrum sound pressure level at this measuring point gradually decreases, exhibiting plateau characteristics in the lower frequency region, with an inflection point around 80Hz.

[0140] The signal analysis and plotting software used in step eight includes MATLAB, Python, etc.

[0141] Step 9: Plot the wavenumber-frequency spectrum of the pulsating pressure data of the pressure measurement array.

[0142] First, pulsating pressure data at specific measurement point array locations on the underwater vehicle are extracted. A two-dimensional Fourier transform is then performed on the pulsating pressure at each array point using signal analysis software. The results of the two-dimensional Fourier transform are then frequency-shifted so that the center of the bilateral spectrum is located at the origin. Finally, a wavenumber-frequency spectrum is plotted using plotting software based on the calculated results, as shown in the attached figure. Figure 12 As shown.

[0143] The wavenumber-frequency spectrum of turbulent fluctuating pressure is defined as the Fourier transform of the correlation function of the spatiotemporal signal of turbulent fluctuating pressure in time and space. Its mathematical expression is:

[0144]

[0145] In practice, the wavenumber-frequency spectrum of turbulent fluctuating pressure is typically obtained by performing a Fast Fourier Transform (FFT) on the discrete-time signal of turbulent fluctuating pressure and then ensemble averaging the squares of its amplitudes. The expression for the wavenumber-frequency spectrum of turbulent fluctuating pressure is as follows:

[0146]

[0147] Where, p m This represents the pulsating pressure measured at the m-th measuring point, where N is the number of time nodes, M is the number of measuring points, Δx is the distance between measuring points, and Δt is the time step.

[0148] The highest spectral level in the wavenumber-frequency spectrum is called the "migrating ridge" of the turbulent fluctuating pressure wavenumber-frequency spectrum. The migrating ridge represents the main distribution region of most of the energy (including energy vortices) in the turbulence. The slope of the migrating ridge, dω / dk = Uc, is defined as the migration velocity, representing the average migration velocity of the vortex structure (quasi-ordered structure) in the turbulence. It is closely related to the migration and transfer of energy within the vortex structure (quasi-ordered structure) in the turbulence. Studying the wavenumber-frequency spectrum of turbulent fluctuating pressure can reveal the distribution law of turbulent energy and the influence of changes in external conditions on the turbulent energy distribution law, thereby guiding practical engineering applications related to turbulent flow.

[0149] The signal analysis and plotting software used in step nine includes MATLAB, Python, etc.

[0150] Step 10: Based on the results of different modes and vortices of different scales of the vorticity field obtained by the dynamic modal decomposition technique in Step 5, and the spectral results of the pulsating pressure measurement points in Step 6, establish the correlation between vortices of different scales of the flow field and the high and low frequencies of the sound source according to the modal frequency correspondence, and summarize the correlation rules. For this data, the different modes of the vorticity field corresponding to frequencies of 40.95Hz and 110.53Hz are shown in the appendix. Figure 13 and attached Figure 14 As shown, it can be seen that the size of the vortex in the flow field corresponding to 40.95Hz is greater than that corresponding to 110.53Hz. This reflects that the large-scale vortex in the flow field is a low-frequency sound source, while the small-scale vortex reflects the high-frequency sound source in the flow field.

[0151] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0152] Example 2

[0153] In an exemplary embodiment, an electronic device is also provided, comprising:

[0154] At least one processor; and

[0155] A memory that is communicatively connected to the at least one processor;

[0156] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, cause the electronic device to perform a method for analyzing the correlation between the pulsating flow field and sound sources of an underwater vehicle.

[0157] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0158] In an exemplary embodiment, a storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements a method for analyzing the correlation between the pulsating flow field and sound sources of an underwater vehicle.

[0159] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0160] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing the correlation between the pulsating flow field and sound source of an underwater vehicle, characterized in that, Includes the following steps: Step 1: Obtain flow field simulation information based on the SUBOFF standard model in the simulation environment; Step 2: Draw a velocity flow field cloud map of the pulsating flow field based on the flow field simulation information; Step 3: Based on the numerical simulation results of the flow field, calculate and determine the streamlines of the flow field according to the definition of streamlines, and draw the streamline distribution diagram corresponding to the longitudinal section of the underwater vehicle. Step 4: Based on the velocity contour maps and streamline diagrams obtained in Steps 2 and 3, and the resulting flow field diagrams showing the distribution of velocity magnitude and direction, the third-generation vortex identification method, Liutex, is used to calculate the vortex structure of the flow field, calculating the vortex distribution at each coordinate position. extract isosurfaces, select Isosurface visualization of vortex structure in flow field; Step 5: Use dynamic mode decomposition technology to decompose... The field yields different modes and scales of vorticity field; Step 6: Based on the flow field simulation information corresponding to continuous time points, extract the pulsating pressure data at specific measuring points of the underwater vehicle, and use plotting software to draw the time-history curve of the pulsating pressure data at the pressure measuring points; Step 7: The pulsating pressure data at specific measuring points of the underwater vehicle is analyzed using spectral analysis. The pulsating pressure signal is then subjected to Fourier transform or power spectral density estimation using signal analysis software, and the spectrum of the pulsating pressure data at the measuring point is plotted. Step 8: Plot the 1 / 3 octave spectrum of the pulsating pressure data at the pressure measurement points; Step 9: Plot the wavenumber-frequency spectrum of the pulsating pressure data from the pressure measurement array; Step 10: Based on the results of different modes and different scales of vorticity field obtained by the dynamic mode decomposition technique in Step 5, and the spectrum results of the pulsating pressure measurement points in Step 6, establish the correlation between vortices of different scales in the flow field and the high and low frequencies of the sound source according to the modal frequency correspondence, and summarize the correlation law.

2. The method for analyzing the correlation between the pulsating flow field and sound source of an underwater vehicle according to claim 1, characterized in that, The flow field simulation information includes the velocity and pressure information of the SUBOFF standard model at various coordinate positions in the flow field environment at the current moment.

3. The method for analyzing the correlation between the pulsating flow field and sound source of an underwater vehicle according to claim 2, characterized in that, The vortex structure of the flow field shows that the flow around the underwater vehicle has gone through laminar flow, transition flow and turbulent boundary layer flow states. Most of the surface of the underwater vehicle is a turbulent boundary layer, and a typical hairpin-shaped vortex structure can be observed, reflecting the complex pulsating flow field near the wall of the underwater vehicle.

4. The method for analyzing the correlation between the pulsating flow field and sound source of an underwater vehicle according to claim 2, characterized in that, The logic for obtaining the visualized flow field vortex structure is as follows: In the initial xyz coordinate system, the flow field simulation velocity information is obtained based on the simulation environment. The velocity gradient tensor ▽v and its eigenvalues ​​are calculated. When the velocity gradient tensor has two complex conjugate eigenvalues, it indicates that the instantaneous streamline is circular or spiral. The velocity gradient tensor is marked as a vortex structure. The velocity gradient tensor is normalized using Liutex to obtain a vortex identification method that does not depend on the isosurface threshold. The normalized Liutex expression is as follows:

5. The method for analyzing the correlation between the pulsating flow field and sound source of an underwater vehicle according to claim 4, characterized in that, The specific decomposition logic of the dynamic mode decomposition technique is as follows: For unsteady flow field time series, the N snapshots obtained through experiments or numerical simulations can be written as a snapshot sequence from time 1 to N, i.e., {x1, x2, x3, ..., x...} N }, where the snapshot at time i is represented as a column vector x. i And the time interval between any two snapshots is Δt; Assuming the flow field x i+1 Through the flow field x i The linear mapping representation is: x i+1 =Ax i ; Where A is the system matrix of the high-dimensional flow field; if the dynamic system itself is nonlinear, then this process is a linear estimation process. Based on the assumed linear mapping relationship, matrix A can reflect the dynamic characteristics of the system. A can be calculated from the data sequence by reducing the order. Using flow field snapshots from time 1 to N, two snapshot matrices X = [x1, x2, x3, ..., xn] can be constructed. N-1 ] and Y = [x2, x3, x4, ..., x N Based on the assumptions in the above formula, we can conclude that: Y=[x2,x3,x4,...,x N ] =[Ax1,Ax2,Ax3,...,Ax N-1 ]=AX; By performing mathematical transformations on the above snapshot matrix, the dominant eigenvalues ​​and main modes are extracted. As the number of snapshots N increases, the data sequence {x1,x2,x3,...,x...} is further analyzed. N It has been able to capture the main physical characteristics.

6. The method for analyzing the correlation between the pulsating flow field and sound source of an underwater vehicle according to claim 5, characterized in that, The analysis logic for the spectrum results of the signal is as follows: The spectrum is divided into a series of 1 / 3 octave bands, and the width of each frequency band is 1 / 3 of the width of the previous frequency band. Frequency bands are typically divided using a logarithmic scale, and the energy within each frequency band is calculated. The energy within each frequency band is normalized using a logarithmic scale, and the 1 / 3 octave spectrum of the pulsating pressure data at the pressure measurement point is plotted using plotting software; thus, the spectral result of the signal is obtained.

7. The method for analyzing the correlation between the pulsating flow field and sound source of an underwater vehicle according to claim 6, characterized in that, The pulsating pressure data at specific measurement point array locations of the underwater vehicle are extracted. The pulsating pressure at each measurement point of the array is subjected to a two-dimensional Fourier transform using signal analysis software. The results of the two-dimensional Fourier transform are frequency shifted so that the center of the two-sided spectrum is located at the origin. Finally, the wavenumber-frequency spectrum is plotted using plotting software based on the calculated results.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, cause the electronic device to perform the underwater vehicle pulsating flow field and sound source correlation analysis method according to any one of claims 1-6.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a method for analyzing the correlation between the pulsating flow field and the sound source of an underwater vehicle as described in any one of claims 1-6.

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