Vehicle chassis wind noise optimization method and device, computer equipment, medium and product

Abstract

The invention relates to the technical field of noise control, and discloses a vehicle chassis wind noise optimization method and device, computer equipment, a medium and a product, and the method comprises the steps: carrying out the dynamic mode decomposition of obtained flow field data, obtaining the flow field mode characteristics under different frequencies, screening out the low-frequency mode characteristics of middle and low frequencies, and carrying out the optimization of the low-frequency mode characteristics; therefore, the space-time evolution characteristics of the low and medium frequency wind noise in the vehicle chassis flow field can be accurately analyzed. Furthermore, the flow-induced load characteristic and the sound-induced load characteristic in each low-frequency modal characteristic are decoupled to obtain the sound-induced load characteristic in each low-frequency modal characteristic, so that the influence of airflow fluctuation, structural vibration and the like caused by the flow-induced load on the wind noise analysis is avoided; therefore, the positioning accuracy of the sound source and the propagation path of the wind noise waves is improved. And finally, in combination with the sound source and the propagation path of the wind noise wave, the low and medium frequency wind noise of the vehicle chassis is optimized, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the field of noise control technology, and in particular to a vehicle chassis wind noise optimization method, device, computer equipment, medium and product. Background Art

[0002] With the development of new energy vehicles, the impact of traditional mechanical noise is becoming smaller and smaller, while wind noise, which increases at a 5.8th power with vehicle speed, is becoming more and more prominent. How to control vehicle wind noise has become a key issue in improving user experience.

[0003] Currently, existing technologies often use flow field simulation to generate wind noise simulation results. Based on these wind noise simulation results, the surface profiles of components such as rearview mirrors and vehicle A-pillars are modified to improve wind noise. However, current efforts to control vehicle wind noise primarily focus on body noise. However, test results show that chassis wind noise is primarily low- and medium-frequency noise, and the sound absorption and insulation properties of the acoustic package installed in the passenger compartment are unlikely to suppress low- and medium-frequency noise in the chassis area. Furthermore, the flow-induced load in the vehicle chassis area changes the chassis flow field characteristics, which can easily affect the propagation characteristics of the acoustic load, resulting in low accuracy in the sound source location and propagation path determined based on wind noise simulation results. Summary of the Invention

[0004] In view of this, the present invention provides a vehicle chassis wind noise optimization method, device, computer equipment, medium and product to solve the problem that the existing technology cannot accurately analyze the sound source location and propagation path of noise under the influence of flow-induced load on acoustic load.

[0005] In a first aspect, the present invention provides a method for optimizing vehicle chassis wind noise, the method comprising:

[0006] Perform chassis wind noise simulation on the vehicle model to obtain flow field data in the vehicle chassis area;

[0007] Performing dynamic modal decomposition based on the flow field data to obtain flow field modal features at different frequencies, and screening out at least one low-frequency modal feature with a frequency lower than a preset frequency from the flow field modal features;

[0008] For each low-frequency modal feature, the flow-induced load feature and the acoustic-induced load feature in the low-frequency modal feature are decoupled to obtain the acoustic-induced load feature in the low-frequency modal feature;

[0009] Based on the acoustic load characteristics, the sound source and / or propagation path of the wind noise wave in the vehicle chassis area is determined, and based on the sound source and / or propagation path, the vehicle chassis wind noise is optimized.

[0010] This application simulates the chassis wind noise of the whole vehicle model, and obtains the flow field modal characteristics at different frequencies by performing dynamic modal decomposition on the obtained flow field data, from which the low-frequency modal characteristics of the medium and low frequencies are screened out, so as to accurately analyze the spatiotemporal evolution characteristics of the medium and low frequency wind noise in the vehicle chassis flow field. Furthermore, the flow-induced load characteristics and acoustic load characteristics in each low-frequency modal characteristic are decoupled to obtain the acoustic load characteristics in each low-frequency modal characteristic, thereby avoiding the influence of airflow fluctuations and structural vibrations caused by the flow-induced load on the wind noise analysis, thereby improving the positioning accuracy of the sound source and propagation path of the wind noise wave. Finally, combined with the sound source and propagation path of the wind noise wave, the medium and low frequency wind noise of the vehicle chassis is optimized to improve the user experience.

[0011] In an optional embodiment, decoupling the flow-induced load feature and the acoustic-induced load feature in the low-frequency modal feature to obtain the acoustic-induced load feature in the target low-frequency modal feature includes:

[0012] According to the low-frequency modal characteristics, the spatial variation characteristics of the flow field pressure are obtained;

[0013] The wave number characteristics of the flow-induced load and the acoustic-induced load at the target frequency corresponding to the low-frequency modal characteristics are determined. The spatial variation characteristics of the flow field pressure are filtered based on the wave number characteristics to obtain the acoustic-induced load characteristics in the low-frequency modal characteristics.

[0014] The present application filters the spatial variation characteristics of the flow field pressure in the low-frequency modal characteristics through the wave number characteristics of the flow-induced load and the acoustic load, thereby decoupling the flow-induced load and the acoustic load in the spatial variation characteristics, extracting the acoustic load characteristics, and avoiding the influence of the airflow fluctuations and structural vibrations caused by the flow-induced load on the wind noise analysis, thereby improving the positioning accuracy of the sound source and propagation path of the sound wave.

[0015] In an optional embodiment, filtering the spatial variation characteristics of the flow field pressure based on the wave number characteristics to obtain the acoustic load characteristics in the low-frequency modal characteristics includes:

[0016] determining a first wavelength of the flow-induced load and a second wavelength of the acoustic-induced load based on the wave number characteristics;

[0017] The flow-induced load features in the spatial variation features of the flow field pressure are filtered out based on the first wavelength, and the acoustic load features in the spatial variation features of the flow field pressure are extracted based on the second wavelength.

[0018] The present application determines the first wavelength of the flow-induced load and the second wavelength of the acoustic load, thereby identifying the flow-induced load characteristics and the acoustic load characteristics in the spatial variation characteristics of the flow field pressure, achieving decoupling of the flow-induced load and the acoustic load, and obtaining the acoustic load characteristics in the spatial variation characteristics of the flow field pressure, thereby avoiding the influence of airflow fluctuations, structural vibrations, etc. caused by the flow-induced load on wind noise analysis, thereby improving the positioning accuracy of the sound source and propagation path of the sound wave.

[0019] In an optional embodiment, determining the sound source and / or propagation path of the wind noise wave in the vehicle chassis area based on the acoustic load characteristics includes:

[0020] According to the characteristics of the acoustic load, the temporal and spatial variation characteristics of the acoustic load are obtained;

[0021] Based on the spatiotemporal variation characteristics, the propagation velocity of the acoustic load at multiple consecutive moments is determined;

[0022] Reverse positioning is performed based on the vector directions corresponding to the propagation velocities at multiple consecutive moments to obtain the sound source and / or propagation path of the wind noise wave in the vehicle chassis area.

[0023] The present application determines the vector directions corresponding to the propagation velocities of the acoustic load at multiple consecutive moments through the temporal and spatial variation characteristics of the acoustic load, and then obtains the sound source and / or propagation path of the wind noise wave in the vehicle chassis area through reverse positioning, thereby eliminating the influence of flow-induced loads such as airflow fluctuations and structural vibrations caused by flow-induced loads on the analysis of wind noise sources and propagation paths, thereby improving the accuracy of wind noise analysis.

[0024] In an optional embodiment, determining the sound source and / or propagation path of the wind noise wave in the vehicle chassis area based on the acoustic load characteristics includes:

[0025] According to the acoustic load characteristics, the temporal and spatial variation characteristics of the acoustic load are obtained, and based on the temporal and spatial variation characteristics, the pressure contour corresponding to the acoustic load at the target time is determined;

[0026] Based on the pressure cloud map, multiple isobars at the target time are determined;

[0027] According to the plurality of isobars, a sound source and / or a propagation path of the wind noise wave in the vehicle chassis area is obtained.

[0028] This application determines the pressure cloud map corresponding to the acoustic load at the target time, extracts multiple isobars, and determines the distribution of the pressure fluctuations of the sound waves in the vehicle chassis area. Then, combined with the common center and / or evolution process of the multiple isobars, the sound source and / or propagation path of the wind noise wave in the vehicle chassis area is determined, so as to control the wind noise in the vehicle chassis area and improve the user experience.

[0029] In an optional embodiment, optimizing vehicle chassis wind noise based on the sound source and / or propagation path includes:

[0030] Suppress the formation process of sound sources;

[0031] And / or, blocking the propagation path.

[0032] The present application optimizes the wind noise in the vehicle chassis area by suppressing the formation process of the sound source of wind noise waves of each frequency and / or blocking the propagation path, thereby achieving the effect of reducing chassis wind noise, which is beneficial to improving the user's driving experience.

[0033] In an optional embodiment, dynamic modal decomposition is performed based on the flow field data to obtain flow field modal characteristics at different frequencies, including:

[0034] According to the flow field data, a first snapshot matrix of the current moment and a second snapshot matrix of the previous moment are constructed;

[0035] Performing singular value decomposition on the second snapshot matrix to obtain a first decomposition matrix;

[0036] Based on the first decomposition matrix, construct a mapping relationship matrix between the first snapshot matrix and the first decomposition matrix;

[0037] Perform dimension reduction on the mapping relationship matrix to obtain the main mode matrix of the mapping relationship matrix;

[0038] Decomposing the mapping relationship matrix after dimensionality reduction to obtain a second decomposition matrix;

[0039] Based on the main modal matrix and the second decomposition matrix, the modal characteristics of the flow field at different frequencies are obtained.

[0040] This application performs dynamic modal decomposition on the flow field data and linearly expresses the high-dimensional flow field data in a low-dimensional space. Each dimension represents a frequency, and then extracts the spatiotemporal evolution characteristics of the flow field load at different frequencies, and then analyzes the spatiotemporal evolution characteristics of the flow-induced load and the acoustic-induced load.

[0041] In an optional embodiment, chassis wind noise simulation is performed on the entire vehicle model to obtain flow field data of the vehicle chassis area, including:

[0042] Perform steady-state simulation and transient simulation on the vehicle model to obtain chassis wind noise simulation data;

[0043] The pressure field data and velocity field data in the chassis wind noise simulation data are extracted to obtain the flow field data of the vehicle chassis area.

[0044] This application extracts pressure field data and velocity field data from chassis wind noise simulation data by performing steady-state simulation and transient simulation on the whole vehicle model, thereby constructing flow field data in the vehicle chassis area to provide data support for the analysis of the sound source and propagation path of vehicle chassis wind noise.

[0045] In a second aspect, the present invention provides a vehicle chassis wind noise optimization device, the device comprising:

[0046] The first processing module is used to perform chassis wind noise simulation on the vehicle model to obtain flow field data of the vehicle chassis area;

[0047] The second processing module is used to perform dynamic modal decomposition based on the flow field data to obtain flow field modal features at different frequencies, and to screen out at least one low-frequency modal feature having a frequency lower than a preset frequency from the flow field modal features;

[0048] a third processing module, configured to decouple the flow-induced load feature and the acoustic-induced load feature in each low-frequency modal feature, to obtain the acoustic-induced load feature in the low-frequency modal feature;

[0049] The fourth processing module is used to determine the sound source and propagation path of the wind noise wave in the vehicle chassis area based on the acoustic load characteristics, and optimize the vehicle chassis wind noise based on the sound source and / or propagation path.

[0050] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the vehicle chassis wind noise optimization method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0051] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the vehicle chassis wind noise optimization method of the first aspect or any corresponding embodiment thereof.

[0052] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the vehicle chassis wind noise optimization method of the first aspect or any corresponding embodiment thereof.

[0053] The beneficial effects of the present invention are:

[0054] This application performs dynamic modal decomposition on the obtained flow field data to obtain the flow field modal characteristics at different frequencies, from which the low-frequency modal characteristics of the medium and low frequencies are screened out in order to accurately analyze the spatiotemporal evolution characteristics of the medium and low frequency wind noise in the vehicle chassis flow field. Furthermore, the flow-induced load characteristics and acoustic load characteristics in each low-frequency modal characteristic are decoupled to obtain the acoustic load characteristics in each low-frequency modal characteristic, thereby avoiding the influence of airflow fluctuations and structural vibrations caused by the flow-induced load on the wind noise analysis, thereby improving the positioning accuracy of the sound source and propagation path of the wind noise wave. Finally, combined with the sound source and propagation path of the wind noise wave, the medium and low frequency wind noise of the vehicle chassis is optimized to improve the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 is a flow chart of a vehicle chassis wind noise optimization method according to an embodiment of the present invention;

[0057] Figure 2 is a flow chart of another vehicle chassis wind noise optimization method according to an embodiment of the present invention;

[0058] Figure 3 is a schematic diagram of a decoupling process according to an embodiment of the present invention;

[0059] Figure 4 is a pressure cloud diagram of a cross section of a vehicle model according to an embodiment of the present invention;

[0060] Figure 5 is a pressure cloud diagram of a longitudinal section of a vehicle model according to an embodiment of the present invention;

[0061] Figure 6 is a schematic diagram of a front wheel fender position according to an embodiment of the present invention;

[0062] Figure 7 This is a comparison diagram of the acoustic effects before and after adding a front wheel baffle according to an embodiment of the present invention;

[0063] Figure 8 is a structural block diagram of a vehicle chassis wind noise optimization device according to an embodiment of the present invention;

[0064] Figure 9 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0065] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0066] According to an embodiment of the present invention, an embodiment of a vehicle chassis wind noise optimization method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0067] In this embodiment, a vehicle chassis wind noise optimization method is provided, which can be applied to devices for performing vehicle chassis wind noise analysis, such as computers, tablet computers, etc. Figure 1 FIG. 1 is a flow chart of a vehicle chassis wind noise optimization method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0068] Step S101 , performing chassis wind noise simulation on the entire vehicle model to obtain flow field data of the vehicle chassis area.

[0069] Specifically, a whole vehicle model is built, which includes a complete chassis model, including detailed lower body structure and data such as suspension, lower guard plate, HVAC system, battery pack, etc., so as to implement chassis wind noise simulation and extract flow field data in the vehicle chassis area.

[0070] In step S102 , dynamic modal decomposition is performed based on the flow field data to obtain flow field modal features at different frequencies, and at least one low-frequency modal feature having a frequency lower than a preset frequency is screened out from the flow field modal features.

[0071] Specifically, dynamic modal decomposition is performed on the flow field data, and the high-dimensional flow field data is converted into a low-dimensional linear space to obtain low-dimensional flow field modal characteristics at different frequencies. Since the vehicle chassis flow field contains flow field excitations of the entire frequency band, the mid- and high-frequency components therein will seriously interfere with, or even obscure, the spatiotemporal evolution characteristics of the mid- and low-frequency flow field excitations, making it difficult to observe the sound source position and excitation characteristics of the chassis flow field, which is not conducive to optimizing and controlling the vehicle chassis flow field excitations. Therefore, the embodiments of the present application screen the flow field modal characteristics at different frequencies to obtain low-frequency modal characteristics, thereby observing the spatiotemporal evolution characteristics of the mid- and low-frequency flow field excitations in the spatiotemporal domain.

[0072] Step S103 : for each low-frequency modal feature, decoupling the flow-induced load feature and the acoustic-induced load feature in the low-frequency modal feature is performed to obtain the acoustic-induced load feature in the low-frequency modal feature.

[0073] Specifically, vehicle chassis flow field excitation generates flow-induced and acoustic-induced loads. Flow-induced loads refer to the pressure exerted directly on the surface of a structure by fluids, such as airflow and turbulence, while acoustic-induced loads refer to the pressure exerted on the surface by sound waves propagating through the fluid. The vehicle chassis is a spatially complex structure, and these flow-induced and acoustic-induced loads are coupled together in low-frequency modal signatures. The frequency of the flow-induced load is generally higher than that of the acoustic load, causing the flow-induced load signature to mask the true acoustic-induced load signature.

[0074] Therefore, this application focuses on the low-frequency modal characteristics at a single frequency, decouples the flow-induced load characteristics and the acoustic load characteristics in the low-frequency modal characteristics, and identifies the acoustic load characteristics in the low-frequency modal characteristics, so as to accurately analyze the spatiotemporal evolution characteristics of medium and low-frequency wind noise in the vehicle chassis flow field.

[0075] Step S104 : determining the sound source and / or propagation path of the wind noise wave in the vehicle chassis area based on the acoustic load characteristics, and optimizing the vehicle chassis wind noise based on the sound source and / or propagation path.

[0076] Specifically, for the acoustic load characteristics in each low-frequency modal feature, the sound source and / or propagation path of the wind noise wave of the corresponding frequency in the vehicle chassis area are determined, and baffles, sound-absorbing and insulating materials, active speakers, etc. are arranged through physical measures to suppress the generation of the sound source or block the propagation of the sound waves, thereby optimizing the medium and low-frequency wind noise in the vehicle chassis.

[0077] The vehicle chassis wind noise optimization method provided in this embodiment simulates the chassis wind noise of the whole vehicle model, and obtains the flow field modal characteristics at different frequencies by performing dynamic modal decomposition on the obtained flow field data. From these, the low-frequency modal characteristics of the medium and low frequencies are screened out to accurately analyze the spatiotemporal evolution characteristics of the medium and low frequency wind noise in the vehicle chassis flow field. Furthermore, the flow-induced load characteristics and acoustic load characteristics in each low-frequency modal characteristic are decoupled to obtain the acoustic load characteristics in each low-frequency modal characteristic, thereby avoiding the influence of airflow fluctuations and structural vibrations caused by the flow-induced load on the wind noise analysis, thereby improving the positioning accuracy of the sound source and propagation path of the wind noise wave. Finally, combined with the sound source and propagation path of the wind noise wave, the medium and low frequency wind noise of the vehicle chassis is optimized to improve the user experience.

[0078] In this embodiment, a vehicle chassis wind noise optimization method is provided, which can be applied to devices for performing vehicle chassis wind noise analysis, such as computers, tablet computers, etc. Figure 2FIG. 1 is a flow chart of a vehicle chassis wind noise optimization method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0079] Step S201 : performing chassis wind noise simulation on the vehicle model to obtain flow field data of the vehicle chassis area.

[0080] Specifically, the above step S201 includes:

[0081] Step S2011: Perform steady-state simulation and transient simulation on the vehicle model to obtain chassis wind noise simulation data.

[0082] Specifically, steady-state and transient simulations of chassis wind noise were performed based on the vehicle model, extracting chassis wind noise simulation data from areas of the chassis with high vorticity and significant pressure fluctuations. It should be noted that steady-state simulation simulates the spatial distribution of physical quantities in the vehicle model, such as pressure and flow velocity, while assuming they remain constant over time. Transient simulation, on the other hand, considers the dynamic changes of the model's physical quantities over time, solving for the states at different moments through time discretization (e.g., setting a time step), ultimately revealing the complete evolution of the physical quantities over time and space.

[0083] In some embodiments, the whole vehicle model is subjected to geometric simplification, meshing, boundary condition setting, etc., and computational fluid dynamics (CFD) can be used to simulate and solve the whole vehicle model to obtain chassis wind noise simulation data. For details, please refer to the detailed description of the relevant technology, which will not be repeated here.

[0084] Step S2012: extracting pressure field data and velocity field data from the chassis wind noise simulation data to obtain flow field data of the vehicle chassis area.

[0085] Specifically, based on the chassis wind noise simulation data of the vehicle model with x = 0.0m (i.e., vehicle cross section) and y = 0.0 (i.e., vehicle longitudinal section), the pressure field data p at time t is sampled at a certain time interval within a fixed time period. t and velocity field data v t , collect the pressure field data and velocity field data from the chassis wind noise simulation data. To ensure the consistency of the pressure field data and the velocity field data, the pressure field data and the velocity field data are stacked vertically to obtain the joint space vector at time t The flow field data is constructed according to the joint space vectors at multiple moments, and the extracted flow field data is converted into a matrix form S = [s1, s2, ..., s k-2 , s k-1 , s k], where S represents the matrix form of flow field data and k is the sampling number, i.e. t = 1, 2,…, k-2, k-1, k.

[0086] The embodiment of the present application performs steady-state simulation and transient simulation on the whole vehicle model to extract pressure field data and velocity field data from the chassis wind noise simulation data, thereby constructing flow field data in the vehicle chassis area to provide data support for the analysis of the sound source and propagation path of the vehicle chassis wind noise.

[0087] Step S202 : performing dynamic modal decomposition according to the flow field data to obtain flow field modal features at different frequencies, and screening out at least one low-frequency modal feature having a frequency lower than a preset frequency from the flow field modal features.

[0088] In some optional implementations, the above step S202 includes:

[0089] Step a1: constructing a first snapshot matrix at the current moment and a second snapshot matrix at the previous moment according to the flow field data.

[0090] Specifically, according to the matrix form of flow field data S = [s1, s2, ..., s k-2 , s k-1 , s k ], construct the first snapshot matrix at the current moment And the second snapshot matrix of the previous moment

[0091] Step a2: performing singular value decomposition on the second snapshot matrix to obtain a first decomposition matrix.

[0092] Specifically, assume that the joint space vector s at any adjacent time t+1 and s t There is an initial mapping relationship matrix H between them, then:

[0093]

[0094] Among them, the eigenvalues ​​and eigenvectors of the initial mapping relationship matrix H can reflect the dynamic characteristics of the flow field data in the vehicle chassis area. Furthermore, in order to solve the eigenvalues ​​and eigenvectors of the initial mapping relationship matrix H, the second snapshot matrix is ​​first subjected to singular value decomposition to obtain the first decomposition matrix:

[0095]

[0096] Where S represents a left singular matrix, W is a diagonal singular matrix, V is a right singular matrix, and both S and V are unit orthogonal matrices.

[0097] Step a3: constructing a mapping relationship matrix between the first snapshot matrix and the first decomposition matrix based on the first decomposition matrix.

[0098] Specifically, by substituting equation (2) into equation (1), we can obtain the mapping relationship matrix A between the first snapshot matrix and the first decomposition matrix:

[0099]

[0100] Step a4: reduce the dimension of the mapping relationship matrix to obtain the main mode matrix of the mapping relationship matrix.

[0101] Specifically, in order to efficiently decouple the mapping relationship matrix A, the main modes of the first r orders of the mapping relationship matrix A are retained, that is, formula (3) becomes:

[0102]

[0103] in, is the main modal matrix of the mapping relationship matrix A, S r is the main mode of the first r orders of the left singular matrix, V r is the main mode of the first r orders of the right singular matrix, W r is the main mode of the first r orders of the diagonal singular matrix.

[0104] Step a5: decompose the mapping relationship matrix after dimensionality reduction to obtain a second decomposition matrix.

[0105] Specifically, the mapping relationship matrix after dimensionality reduction retains the main modes of the first r orders of the mapping relationship matrix A. right Perform QR decomposition to obtain the second decomposition matrix:

[0106]

[0107] Where P represents the eigenvector matrix and Λ represents the eigenvalue matrix.

[0108] Step a6: Based on the main modal matrix and the second decomposition matrix, obtain the flow field modal characteristics at different frequencies.

[0109] Specifically, the joint space vector s at any time t It can be expressed as:

[0110]

[0111] Furthermore, by definition, the modal matrix φ = S r P, modal amplitude matrix a = φ -1 s1, then:

[0112]

[0113] Where j represents a single frequency, δ jis the growth rate or decay rate, w j is the angular frequency, λ j is the characteristic value, and Δt is the time interval. If you want to extract the flow field information at a certain frequency or several frequencies, you can adjust the j value in equation (7) to obtain the flow field information at a single frequency. You can also obtain the flow field results of multiple frequencies by superimposing multiple j values, and thus obtain the flow field modal characteristics at different frequencies.

[0114] The embodiment of the present application performs dynamic modal decomposition on the flow field data, linearly expressing the high-dimensional flow field data in a low-dimensional space, where each dimension represents a frequency, and then extracts the spatiotemporal evolution characteristics of the flow field load at different frequencies, and then analyzes the spatiotemporal evolution characteristics of the flow-induced load and the acoustic-induced load.

[0115] Step a7: Screen out at least one low-frequency modal feature having a frequency lower than a preset frequency from the flow field modal features.

[0116] Specifically, for the angular frequency w in equation (7), j Limitation is performed to extract the modal characteristics of the flow field at medium and low frequencies. For example, the preset frequency can be 500 Hz. In order to eliminate the medium and high frequency components in the chassis flow field, only the frequency components with a frequency less than 500 Hz in formula (7) are extracted, where the frequency f = 2π / w j , and obtain at least one low-frequency modal feature, thereby observing the spatiotemporal evolution characteristics of the medium and low-frequency components in the spatiotemporal domain.

[0117] Step S203 : for each low-frequency modal feature, decoupling the flow-induced load feature and the acoustic-induced load feature in the low-frequency modal feature to obtain the acoustic-induced load feature in the low-frequency modal feature.

[0118] Specifically, for each low-frequency modal feature, step S203 includes:

[0119] Step S2031: Obtain spatial variation characteristics of flow field pressure based on low-frequency modal characteristics.

[0120] Specifically, the flow-induced load and the acoustic-induced load are coupled to form a flocculent structure. To observe the spatiotemporal evolution characteristics of the flocculent structure of the flow field at a single frequency, we fix w in Eq. (7) j The j value is used to obtain the low-frequency modal characteristics at a single frequency, that is, to restore the flow field pressure s at a single frequency. ft The corresponding formula for the results of changes over time and space is as follows:

[0121]

[0122] Where j is a constant.

[0123] Furthermore, based on formula (8), the flow field pressure s can be obtainedft The spatial variation characteristics at a certain moment. j Taking 100Hz as an example, its spatial variation characteristics at time T0 and time T1 are as follows Figure 3 shown.

[0124] Step S2032: Determine the wave number characteristics of the flow-induced load and the acoustic-induced load at the target frequency corresponding to the low-frequency modal characteristics, filter the spatial variation characteristics of the flow field pressure based on the wave number characteristics, and obtain the acoustic-induced load characteristics in the low-frequency modal characteristics.

[0125] Specifically, according to the wave number definition: in, represents the wave number, f represents the frequency, and v represents the propagation velocity. The propagation velocity of the flow-induced load is approximately Mach 0.1, and the propagation velocity of the acoustic load is approximately Mach 1. Therefore, at the same frequency, the wave number characteristics of the flow-induced load and the acoustic load differ significantly (in the same time interval, the pressure propagation distance of the acoustic load is greater than that of the flow-induced load). The propagation characteristics of acoustic loads, such as sound waves, and flow-induced loads, such as airflow pulsations, in space and time are significantly different. Therefore, the wave number characteristics can be used to decouple the flow-induced and acoustic loads in the low-frequency modal characteristics, thereby extracting the acoustic load characteristics from the low-frequency modal characteristics.

[0126] In some embodiments, a first wavelength of the flow-induced load and a second wavelength of the acoustic load are determined based on the wave number characteristics, the flow-induced load characteristics in the spatial variation characteristics of the flow field pressure are filtered out based on the first wavelength, and the acoustic load characteristics in the spatial variation characteristics of the flow field pressure are extracted based on the second wavelength.

[0127] Specifically, by taking the derivative of the wave number, we can obtain the first wavelength of the flow-induced load and the second wavelength of the acoustic load, where the first wavelength is about 0.33 m, the second wavelength γ is about 3.4 m, and the second wavelength is about 10 times the first wavelength. Figure 3 Taking the spatial variation characteristics of the flow field pressure at time T1 as an example, by calculating the curve envelope surface at time T1, the flow-induced load characteristics of the first wavelength can be filtered out, and the acoustic-induced load characteristics of the second wavelength can be extracted to achieve the decoupling of the flow-induced load and the acoustic-induced load, and obtain the acoustic-induced load characteristics.

[0128] Related technologies use wavenumber decomposition to perform a spatial Fourier transform on a plane pressure field to identify acoustic pressure and turbulent pressure. However, this is based on the physical principle that the wavelength of acoustic waves is much smaller than the wavelength of turbulent pulsations. This means that traditional wavenumber decomposition methods rely heavily on high-frequency scenarios. In the vehicle chassis area, the wavelength of low-frequency acoustic waves increases significantly (for example, the wavelength of a 100Hz sound wave in air is approximately 3.4m), highly overlapping with the wavelength of large-scale turbulent structures. Therefore, it is impossible to effectively separate acoustic and flow-induced loads in the wavenumber domain.

[0129] Furthermore, traditional wavenumber decomposition methods require a manually set cutoff wavenumber, treating wavenumbers above the cutoff wavenumber as turbulent components and those below the cutoff wavenumber as acoustic components. In the low-frequency region, if the cutoff wavenumber is set too low, large-scale turbulence can be easily misidentified as acoustic components, resulting in inaccurate cutoff wavenumbers. Furthermore, traditional wavenumber decomposition methods only produce results in the frequency or wavenumber domain, failing to identify the dynamic evolution of acoustic loading over time and space.

[0130] Compared with the traditional wave number decomposition method, the embodiment of the present application determines the first wavelength of the flow-induced load and the second wavelength of the acoustic load, thereby identifying the flow-induced load characteristics and the acoustic load characteristics in the spatial variation characteristics of the flow field pressure, realizing the decoupling of the flow-induced load and the acoustic load, and obtaining the acoustic load characteristics in the spatial variation characteristics of the flow field pressure, thereby avoiding the influence of airflow fluctuations, structural vibrations, etc. caused by the flow-induced load on the wind noise analysis, thereby improving the positioning accuracy of the sound source and propagation path of the sound wave.

[0131] Step S204 : determining the sound source and / or propagation path of the wind noise wave in the vehicle chassis area based on the acoustic load characteristics, and optimizing the vehicle chassis wind noise based on the sound source and / or propagation path.

[0132] Specifically, the above step S204 includes:

[0133] Step S2041 : determining the sound source and / or propagation path of the wind noise wave in the vehicle chassis area based on the acoustic load characteristics.

[0134] In some optional embodiments, temporal and spatial variation characteristics of the acoustic load are obtained based on the acoustic load characteristics. Based on the temporal and spatial variation characteristics, the propagation velocity of the acoustic load at multiple consecutive moments is determined. Inverse positioning is performed based on the vector directions corresponding to the propagation velocities at multiple consecutive moments to obtain the sound source and / or propagation path of the wind noise wave in the vehicle chassis area.

[0135] Specifically, the temporal and spatial variation characteristics of the acoustic load are obtained through the acoustic load characteristics, and the temporal and spatial variation results of the pressure fluctuations corresponding to the acoustic load can be determined. By calculating the propagation distance of the pressure fluctuations of the acoustic load within adjacent time intervals Δt, the vector direction corresponding to the propagation speed of the acoustic load can be obtained. Reverse positioning is performed based on the vector directions corresponding to multiple consecutive moments, and the sound source and / or propagation path of the wind noise wave in the vehicle chassis area can be determined.

[0136] The embodiment of the present application determines the vector directions corresponding to the propagation velocities of the acoustic load at multiple consecutive moments through the temporal and spatial variation characteristics of the acoustic load, and then obtains the sound source and / or propagation path of the wind noise wave in the vehicle chassis area through reverse positioning, thereby eliminating the influence of flow-induced loads such as airflow fluctuations and structural vibrations caused by flow-induced loads on the analysis of wind noise sources and propagation paths, thereby improving the accuracy of wind noise analysis.

[0137] In some optional embodiments, based on the acoustic load characteristics, the spatiotemporal variation characteristics of the acoustic load are obtained, and based on the spatiotemporal variation characteristics, a pressure cloud map corresponding to the acoustic load at a target time is determined; based on the pressure cloud map, multiple isobars at the target time are determined; based on the multiple isobars, the sound source and / or propagation path of the wind noise wave in the vehicle chassis area is obtained.

[0138] Specifically, the temporal and spatial variation characteristics of the acoustic load are visualized. Taking the temporal and spatial variation characteristics of the acoustic load at 100 Hz as an example, the pressure cloud diagram of the cross section of the vehicle model x = 0.0m at different times of the period T is as follows: Figure 4 As shown in the figure, analyzing the pressure contour at time 0.2T reveals cross-sectional areas with acoustic pressure values ​​of 0.5Pa, 0.4Pa, 0.3Pa, and so on. This allows the identification of multiple isobars within the cross-sectional pressure contour at time 0.2T. By determining the common center of these multiple isobars, the source of the wind noise wave within the vehicle chassis can be determined. By analyzing the direction of the acoustic pressure drop based on the evolution of these multiple isobars at each moment, the propagation path of the wind noise wave within the vehicle chassis can be determined.

[0139] The embodiment of the present application determines the pressure cloud map corresponding to the acoustic load at the target time, extracts multiple isobars, and determines the distribution of the pressure fluctuations of the sound waves in the vehicle chassis area. Then, combined with the common center and / or evolution process of the multiple isobars, the sound source and / or propagation path of the wind noise wave in the vehicle chassis area is determined, so as to control the wind noise in the vehicle chassis area and improve the user experience.

[0140] In some embodiments, the temporal and spatial variation characteristics of the acoustic load are visualized. Taking the temporal and spatial variation characteristics of the acoustic load at 100 Hz as an example, the pressure cloud diagram of the longitudinal section of the vehicle model y = 0.0 m at different times of period T is as follows: Figure 5 As shown. Combined Figure 4 and Figure 5 The pressure contour map shown above can be used to identify multiple isosurfaces of the acoustic load at the target time (for example, corresponding to pressure values ​​of 0.5 Pa, 0.4 Pa, and 0.3 Pa). By determining the common center of these isosurfaces, the source of the wind noise wave in the vehicle chassis area can be determined. By combining the multiple isosurfaces corresponding to each time point and analyzing the direction of the sound wave pressure drop, the propagation path of the wind noise wave in the vehicle chassis area can be determined.

[0141] Combine Figure 4 and Figure 5 As shown, the greater the pressure fluctuation in the corresponding area, the lighter the color in the pressure cloud map. By analyzing the color change, the propagation path of the sound wave can be determined. It can be found that the sound source area of ​​the wind noise wave is mainly in the left and right front wheel arches in the vehicle chassis area. The main propagation path of the sound wave is: the sound source radiates noise from the front wheel arch to the surrounding area, and some sound waves will invade the cabin from the gap between the wheel arch and the cabin, and then some sound waves will propagate downstream through the channel between the battery pack and the floor until they collide with the rear inner wall and reflect the sound wave.

[0142] Step S2042: suppressing the formation process of the sound source and / or blocking the propagation path.

[0143] Specifically, the sound source and / or propagation path corresponding to the acoustic load characteristics in each low-frequency modal feature can be determined, so as to optimize the control of wind noise at each frequency, for example, improving the sound generation mechanism of the sound source, blocking the propagation path of the sound wave, etc.

[0144] Take 100Hz wind noise optimization control as an example, see again Figure 4 and Figure 5 In order to suppress the formation of sound sources in the front wheel cavity, the airflow impacting the wheel cover, rim and other parts can be reduced to prevent the generation of sound sources, such as Figure 6 As shown, a baffle with a height of 50 mm is set in the area near the left and right front wheel covers in the lower guard plate area of ​​the cabin to improve the impact of airflow on components such as wheel covers and rims, thereby improving the sound generation mechanism of the sound source and suppressing the formation process of the sound source.

[0145] like Figure 7 As shown in the figure, a comparison of the acoustic response at the driver's left ear before and after the addition of the baffle reveals that the total sound pressure level at the driver's left ear decreased by 0.5dBA after the baffle was installed on the front wheel. This indicates that improving the airflow near the wheel cavity helps suppress wind noise sources and improve the acoustic response in the passenger compartment. Furthermore, the present application is particularly effective in improving mid- and low-frequency wind noise.

[0146] In some embodiments, the propagation path of the sound source is blocked, for example, by providing sound-absorbing and insulating materials, thereby improving wind noise.

[0147] The present application optimizes the wind noise in the vehicle chassis area by suppressing the formation process of the sound source of wind noise waves of each frequency and / or blocking the propagation path, thereby achieving the effect of reducing chassis wind noise, which is beneficial to improving the user's driving experience.

[0148] The vehicle chassis wind noise optimization method provided in this embodiment utilizes dynamic modal decomposition to observe the spatiotemporal evolution characteristics of the coherent flocculent structure of acoustic and flow-induced loads at specific frequencies. By leveraging the wavenumber difference between the acoustic and flow-induced loads at the same frequency, the spatiotemporal evolution characteristics of the sound waves in the fluid domain can be clearly observed, thereby determining the source location and transmission path of the wind noise wave. Based on this source location and transmission path, personnel can take measures such as adding baffles and installing sound-absorbing and insulating materials to suppress the sound source and block the propagation of the sound waves, thereby reducing the impact of wind noise on passengers.

[0149] This embodiment also provides a vehicle chassis wind noise optimization device, which is used to implement the above-mentioned embodiments and preferred embodiments. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0150] This embodiment provides a vehicle chassis wind noise optimization device, such as Figure 8 Shown, including:

[0151] The first processing module 801 is used to perform chassis wind noise simulation on the vehicle model to obtain flow field data of the vehicle chassis area;

[0152] The second processing module 802 is configured to perform dynamic modal decomposition based on the flow field data to obtain modal features of the flow field at different frequencies, and to select at least one low-frequency modal feature having a frequency lower than a preset frequency from the modal features of the flow field;

[0153] The third processing module 803 is configured to decouple the flow-induced load feature and the acoustic-induced load feature in each low-frequency modal feature to obtain the acoustic-induced load feature in the low-frequency modal feature;

[0154] The fourth processing module 804 is configured to determine the sound source and propagation path of the wind noise wave in the vehicle chassis area based on the acoustic load characteristics, and optimize the vehicle chassis wind noise based on the sound source and / or propagation path.

[0155] In some optional implementations, the first processing module 801 is further configured to:

[0156] Perform steady-state simulation and transient simulation on the vehicle model to obtain chassis wind noise simulation data;

[0157] The pressure field data and velocity field data in the chassis wind noise simulation data are extracted to obtain the flow field data of the vehicle chassis area.

[0158] In some optional implementations, the second processing module 802 is further configured to:

[0159] According to the flow field data, a first snapshot matrix of the current moment and a second snapshot matrix of the previous moment are constructed;

[0160] Performing singular value decomposition on the second snapshot matrix to obtain a first decomposition matrix;

[0161] Based on the first decomposition matrix, construct a mapping relationship matrix between the first snapshot matrix and the first decomposition matrix;

[0162] Perform dimension reduction on the mapping relationship matrix to obtain the main mode matrix of the mapping relationship matrix;

[0163] Decomposing the mapping relationship matrix after dimensionality reduction to obtain a second decomposition matrix;

[0164] Based on the main mode matrix, eigenvalues ​​and eigenvectors, the modal characteristics of the flow field at different frequencies are obtained.

[0165] In some optional implementations, the third processing module 803 is further configured to:

[0166] According to the low-frequency modal characteristics, the spatial variation characteristics of the flow field pressure are obtained;

[0167] The wave number characteristics of the flow-induced load and the acoustic-induced load at the target frequency corresponding to the low-frequency modal characteristics are determined. The spatial variation characteristics of the flow field pressure are filtered based on the wave number characteristics to obtain the acoustic-induced load characteristics in the low-frequency modal characteristics.

[0168] In some optional implementations, the third processing module 803 is further configured to:

[0169] determining a first wavelength of the flow-induced load and a second wavelength of the acoustic-induced load based on the wave number characteristics;

[0170] The flow-induced load features in the spatial variation features of the flow field pressure are filtered out based on the first wavelength, and the acoustic load features in the spatial variation features of the flow field pressure are extracted based on the second wavelength.

[0171] In some optional implementations, the fourth processing module 804 is further configured to:

[0172] According to the characteristics of the acoustic load, the temporal and spatial variation characteristics of the acoustic load are obtained;

[0173] Based on the spatiotemporal variation characteristics, the propagation velocity of the acoustic load at multiple consecutive moments is determined;

[0174] Reverse positioning is performed based on the vector directions corresponding to the propagation velocities at multiple consecutive moments to obtain the sound source and / or propagation path of the wind noise wave in the vehicle chassis area.

[0175] In some optional implementations, the fourth processing module 804 is further configured to:

[0176] According to the acoustic load characteristics, the temporal and spatial variation characteristics of the acoustic load are obtained, and based on the temporal and spatial variation characteristics, the pressure contour corresponding to the acoustic load at the target time is determined;

[0177] Based on the pressure cloud map, multiple isobars at the target time are determined;

[0178] According to the plurality of isobars, a sound source and / or a propagation path of the wind noise wave in the vehicle chassis area is obtained.

[0179] In some optional implementations, the fourth processing module 804 is further configured to:

[0180] Suppress the formation process of sound sources;

[0181] And / or, blocking the propagation path.

[0182] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0183] The vehicle chassis wind noise optimization device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0184] The embodiment of the present invention also provides a computer device having the above Figure 8 The vehicle chassis wind noise optimization device shown.

[0185] See also Figure 9 , Figure 9 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 9 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 9A processor 10 is taken as an example.

[0186] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0187] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0188] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0189] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0190] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 9 The bus connection is taken as an example.

[0191] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display, and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0192] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0193] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0194] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A vehicle chassis wind noise optimization method, characterized in that: The method comprises: Perform chassis wind noise simulation on the vehicle model to obtain flow field data in the vehicle chassis area; Performing dynamic modal decomposition according to the flow field data to obtain flow field modal features at different frequencies, and screening out at least one low-frequency modal feature having a frequency lower than a preset frequency from the flow field modal features; For each low-frequency modal feature, decoupling the flow-induced load feature and the acoustic-induced load feature in the low-frequency modal feature to obtain the acoustic-induced load feature in the low-frequency modal feature; Based on the acoustic load characteristics, the sound source and / or propagation path of the wind noise wave in the vehicle chassis area is determined, and based on the sound source and / or propagation path, the vehicle chassis wind noise is optimized.

2. The method according to claim 1, characterized in that Decoupling the flow-induced load feature and the acoustic-induced load feature in the low-frequency modal feature to obtain the acoustic-induced load feature in the target low-frequency modal feature includes: According to the low-frequency modal characteristics, a spatial variation characteristic of the flow field pressure is obtained; The wave number characteristics of the flow-induced load and the acoustic-induced load at the target frequency corresponding to the low-frequency modal characteristics are determined respectively, and the spatial variation characteristics of the flow field pressure are filtered based on the wave number characteristics to obtain the acoustic-induced load characteristics in the low-frequency modal characteristics.

3. The method according to claim 2, characterized in that The filtering of the spatial variation characteristics of the flow field pressure based on the wave number characteristics to obtain the acoustic load characteristics in the low-frequency modal characteristics includes: determining a first wavelength of the flow-induced load and a second wavelength of the acoustic-induced load based on the wave number characteristics; The flow-induced load features in the spatial variation features of the flow field pressure are filtered out based on the first wavelength, and the acoustic load features in the spatial variation features of the flow field pressure are extracted based on the second wavelength.

4. The method according to claim 1, wherein The determining of the sound source and / or propagation path of the wind noise wave in the vehicle chassis area based on the acoustic load characteristics includes: According to the acoustic load characteristics, obtaining the temporal and spatial variation characteristics of the acoustic load; determining the propagation velocity of the acoustic load at a plurality of consecutive moments based on the spatiotemporal variation characteristics; Reverse positioning is performed based on the vector directions corresponding to the propagation velocities at multiple consecutive moments to obtain the sound source and / or propagation path of the wind noise wave in the vehicle chassis area.

5. The method according to claim 4, characterized in that The determining of the sound source and / or propagation path of the wind noise wave in the vehicle chassis area based on the acoustic load characteristics includes: Obtaining a temporal and spatial variation characteristic of the acoustic load according to the acoustic load characteristic, and determining a pressure contour corresponding to the acoustic load at a target time based on the temporal and spatial variation characteristic; Determining a plurality of isobars at a target time based on the pressure cloud map; According to the plurality of isobars, the sound source and / or propagation path of the wind noise wave in the vehicle chassis area is obtained.

6. The method according to claim 4, characterized in that The optimization of vehicle chassis wind noise based on the sound source and / or propagation path includes: Suppressing the formation process of the sound source; And / or, blocking the propagation path.

7. The method according to any one of claims 1 to 6, characterized in that The dynamic modal decomposition is performed according to the flow field data to obtain the flow field modal characteristics at different frequencies, including: Constructing a first snapshot matrix at a current moment and a second snapshot matrix at a previous moment according to the flow field data; performing singular value decomposition on the second snapshot matrix to obtain a first decomposition matrix; Based on the first decomposition matrix, constructing a mapping relationship matrix between the first snapshot matrix and the first decomposition matrix; Performing dimensionality reduction on the mapping relationship matrix to obtain a main modal matrix of the mapping relationship matrix; Decomposing the mapping relationship matrix after dimensionality reduction to obtain a second decomposition matrix; Based on the main modal matrix and the second decomposition matrix, flow field modal characteristics at different frequencies are obtained.

8. The method according to any one of claims 1 to 6, characterized in that The chassis wind noise simulation is performed on the vehicle model to obtain flow field data of the vehicle chassis area, including: Perform steady-state simulation and transient simulation on the vehicle model to obtain chassis wind noise simulation data; The pressure field data and velocity field data in the chassis wind noise simulation data are extracted to obtain the flow field data of the vehicle chassis area.

9. A vehicle chassis wind noise optimization device, characterized in that: The device comprises: The first processing module is used to perform chassis wind noise simulation on the vehicle model to obtain flow field data of the vehicle chassis area; a second processing module, configured to perform dynamic modal decomposition according to the flow field data to obtain modal features of the flow field at different frequencies, and to screen out at least one low-frequency modal feature having a frequency lower than a preset frequency from the modal features of the flow field; a third processing module, configured to decouple, for each low-frequency modal feature, a flow-induced load feature and an acoustic-induced load feature in the low-frequency modal feature to obtain an acoustic-induced load feature in the low-frequency modal feature; The fourth processing module is used to determine the sound source and propagation path of the wind noise wave in the vehicle chassis area based on the acoustic load characteristics, and optimize the vehicle chassis wind noise based on the sound source and / or propagation path.

10. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the vehicle chassis wind noise optimization method according to any one of claims 1 to 8 by executing the computer instructions.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the vehicle chassis wind noise optimization method according to any one of claims 1 to 8.

12. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the vehicle chassis wind noise optimization method according to any one of claims 1 to 8.

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