Sound Pressure Evaluation Method, Device and Terminal Equipment Based on Model Reduction Boundary Element Method

Abstract

The present application is applicable to the field of acoustic technology, and provides a sound pressure assessment method, apparatus, and terminal device based on the model-reduction boundary element method, including: obtaining a model to be assessed and its corresponding frequency domain of interest; performing order reduction processing on the model to be assessed to generate a low-dimensional reduced-order model corresponding to the model to be assessed; performing frequency sweep calculation on the low-dimensional reduced-order model according to the frequency domain of interest to generate a sound pressure assessment result corresponding to the model to be assessed in the frequency domain of interest, thereby improving the efficiency of sound pressure assessment.

Description

Technical Field

[0001] The present application belongs to the field of acoustic technology, and in particular relates to a sound pressure assessment method, apparatus, terminal device, and storage medium based on a model reduction boundary element method. Background Art

[0002] With the development of modern society, the dynamics and acoustic qualities of structural systems have become important performance evaluation indicators in fields such as aerospace, military equipment, transportation, and the built environment. Examples include noise prediction for launch vehicle fairings, acoustic stealth performance for ships, ride comfort for large aircraft, high-speed trains, and automobiles, and noise control for wind turbines. Therefore, conducting acoustic calculations and studying the acoustic properties of complex structures has crucial theoretical and practical application value. It is an indispensable step in noise level estimation and a prerequisite for achieving acoustic performance optimization design.

[0003] In related technologies, since the objects that require acoustic analysis usually have very complex structures, the storage and calculation requirements for acoustic analysis are too large, and the sound pressure evaluation efficiency is low. Summary of the Invention

[0004] The embodiments of the present application provide a sound pressure assessment method, apparatus, terminal device, and storage medium based on the model reduction boundary element method, which can solve the problem in related technologies that, when performing acoustic problem analysis on an object, the object usually has a very complex structure, resulting in excessive storage and calculation complexity, and low sound pressure assessment efficiency.

[0005] A first aspect of an embodiment of the present application provides a sound pressure assessment method based on a model-reduced boundary element method, the sound pressure assessment method based on a model-reduced boundary element method comprising:

[0006] Obtain the model to be evaluated and its corresponding frequency domain of interest;

[0007] Performing order reduction processing on the model to be evaluated to generate a low-dimensional reduced-order model corresponding to the model to be evaluated;

[0008] A frequency sweep calculation is performed on the low-dimensional reduced-order model according to the frequency domain of interest to generate a sound pressure evaluation result corresponding to the model to be evaluated in the frequency domain of interest.

[0009] A second aspect of an embodiment of the present application provides a sound pressure assessment device based on a model-reduced boundary element method, the sound pressure assessment device based on a model-reduced boundary element method comprising:

[0010] An acquisition module is used to obtain the model to be evaluated and its corresponding frequency domain of interest;

[0011] An order reduction module is used to reduce the order of the model to be evaluated to generate a low-dimensional reduced-order model corresponding to the model to be evaluated;

[0012] A frequency sweep module is used to perform a frequency sweep calculation on the low-dimensional reduced-order model according to the frequency domain of interest, so as to generate a sound pressure evaluation result corresponding to the model to be evaluated in the frequency domain of interest.

[0013] A third aspect of an embodiment of the present application provides a terminal device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the sound pressure assessment method based on the model reduction boundary element method described in the first aspect is implemented.

[0014] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the sound pressure assessment method based on the model reduction boundary element method described in the first aspect is implemented.

[0015] A fifth aspect of the embodiments of the present application provides a computer program product, which, when executed on a terminal device, enables the terminal device to execute the sound pressure assessment method based on the model reduction boundary element method described in the first aspect.

[0016] Compared with the prior art, the embodiments of the present application have the following advantages: by obtaining the model to be evaluated and its corresponding frequency domain of interest, and performing order reduction processing on the model to be evaluated to generate a low-dimensional reduced-order model corresponding to the model to be evaluated, and then performing frequency sweep calculation on the low-dimensional reduced-order model according to the frequency domain of interest to generate the sound pressure evaluation result corresponding to the model to be evaluated in the frequency domain of interest. Therefore, by performing order reduction processing on the model to be evaluated, the model to be evaluated is simplified, and the dimension of the system equation to be evaluated formed by the large-scale model to be evaluated is reduced, thereby reducing the memory requirement during the frequency sweep process, improving the computational efficiency of the frequency sweep analysis, and thus improving the efficiency of the sound pressure evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 1 is a flow chart of a sound pressure assessment method based on a model-reduced boundary element method according to an embodiment of the present application;

[0019] Figure 2 1 is a flow chart of a sound pressure assessment method based on a model-order reduction boundary element method provided in another embodiment of the present application;

[0020] Figure 3 is a grid diagram of a partial area of a model to be evaluated provided in an embodiment of the present application;

[0021] Figure 4 1 is a schematic structural diagram of a sound pressure assessment device based on a model-reduced boundary element method according to an embodiment of the present application;

[0022] Figure 5 It is a structural diagram of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0023] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0024] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0025] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0026] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0027] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0028] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0029] It should be understood that the size of the serial numbers of each step in this embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of this application.

[0030] In related technologies, since the objects that require acoustic analysis usually have very complex structures, the storage and calculation requirements are too large when performing acoustic analysis on them, and the sound pressure evaluation efficiency is low.

[0031] The present application provides a sound pressure assessment method based on the model-reduction boundary element method. The method can obtain the model to be evaluated and its corresponding frequency domain of interest, and perform order reduction processing on the model to be evaluated to generate a low-dimensional reduced-order model corresponding to the model to be evaluated. Then, a frequency sweep calculation is performed on the low-dimensional reduced-order model according to the frequency domain of interest to generate a sound pressure assessment result corresponding to the model to be evaluated in the frequency domain of interest. Thus, by performing order reduction processing on the model to be evaluated, the model to be evaluated is simplified, and the dimension of the system equation to be evaluated formed by the large-scale model to be evaluated is reduced, thereby reducing the memory requirement in the frequency sweep process, improving the computational efficiency of the frequency sweep analysis, and thus improving the efficiency of the sound pressure assessment.

[0032] In one embodiment, referring to Figure 1 , provides a sound pressure assessment method based on the model reduction boundary element method. This method is applied to a terminal as an example and includes the following steps:

[0033] Step 101: Obtain the model to be evaluated and its corresponding frequency domain of interest.

[0034] The model to be evaluated may be a sound field model for which sound pressure evaluation is to be performed, such as a model diagram of a two-dimensional or three-dimensional structure drawn by drawing software, such as AutoCAD (Autodesk Computer Aided Design).

[0035] The frequency domain of interest may be a preset frequency interval or frequency point for frequency response analysis. In actual use, the frequency domain of interest may be set according to the requirements of sound pressure assessment, such as 11 Hz-1000 Hz, which is not limited in this embodiment of the present application.

[0036] In an embodiment of the present application, the model to be evaluated corresponding to the actual sound pressure evaluation scenario can be drawn in advance using drawing software, and the frequency domain of interest can be preset according to the noise frequency range involved in the scenario.

[0037] For example, when the sound pressure evaluation scenario in the embodiment of the present application is to study the acoustic characteristics of an aircraft, a sound field model of the aircraft can be drawn using CAD drawing software as the model to be evaluated, and then the frequency domain of interest can be preset based on the actual noise frequency range generated by the aircraft in actual applications.

[0038] Step 102 : performing order reduction processing on the model to be evaluated to generate a low-dimensional reduced-order model corresponding to the model to be evaluated.

[0039] In a possible implementation method of the embodiment of the present application, the low-dimensional reduced-order model uses Taylor's theorem to expand the acoustic boundary element kernel function, and then based on the characteristic that the amplitude of the boundary element kernel function decays with distance, a sparse system matrix is constructed according to a preset truncation radius to quickly generate an orthogonal basis. That is, for each source point, only a few nearby distribution points with strong interactions are selected for integral calculation, forming a large-scale sparse rather than dense system matrix, thereby avoiding the need to store and solve the original large-scale full-order model in traditional model reduction. After forming a global orthogonal basis, the interaction between the source point and all the distribution points on the boundary is considered, and the system matrix is formed column by column and projected into the subspace spanned by the global orthogonal basis to obtain a low-dimensional reduced-order model. This process has low memory consumption.

[0040] Step 103 : performing frequency sweep calculation on the low-dimensional reduced-order model according to the frequency domain of interest, so as to generate a sound pressure evaluation result corresponding to the model to be evaluated in the frequency domain of interest.

[0041] In an embodiment of the present application, a frequency sweep calculation is performed on the low-dimensional reduced-order model in the frequency domain of interest to solve the low-dimensional reduced-order model and obtain a sound pressure evaluation result corresponding to the model to be evaluated in the frequency domain of interest.

[0042] As an example, the sound pressure evaluation result may be the sound pressure value of each distribution point of the model to be evaluated; or, the sound pressure value of each distribution point of the model to be evaluated may be further analyzed to generate corresponding sound intensity or sound pressure level data.

[0043] As an example, the sound pressure evaluation results can also be visualized to differentiate and display the sound pressure conditions of each area of the model to be evaluated according to the sound pressure value of each distribution point in each boundary unit of the model to be evaluated, so as to clearly and directly see the sound pressure conditions of each area of the model to be evaluated.

[0044] It can be understood that when performing frequency sweep calculations using the generated low-dimensional reduced-order model, the integral calculation only needs to be performed once for the entire frequency range under consideration, thereby improving the computational efficiency compared to traditional methods. The reduced-order model is much smaller than the dimension of the original full-order model in the prior art, so the frequency sweep calculation solution can obtain results almost in real time.

[0045] In the aforementioned sound pressure assessment method based on the model-order reduction boundary element method, the model to be evaluated and its corresponding frequency domain of interest are obtained, and the model is reduced to generate a low-dimensional reduced-order model corresponding to the model to be evaluated. A frequency sweep calculation is then performed on the low-dimensional reduced-order model according to the frequency domain of interest to generate the sound pressure assessment results corresponding to the model to be evaluated in the frequency domain of interest. Thus, by reducing the order of the model to be evaluated, the model to be evaluated is simplified, and the dimensionality of the system equations to be evaluated formed by the large-scale model to be evaluated is reduced. This reduces the memory requirements during the frequency sweep process, improves the computational efficiency of the frequency sweep analysis, and thus improves the efficiency of the sound pressure assessment.

[0046] In one embodiment, referring to Figure 2 , which is a flow chart of another sound pressure assessment method based on the model reduction boundary element method provided in an embodiment of the present application, including:

[0047] Step 201: Obtain the model to be evaluated and its corresponding frequency domain of interest.

[0048] The specific implementation process and principle of the above step 201 can be referred to the detailed description of the above embodiment and will not be repeated here.

[0049] Step 202 : dividing the boundary of the model to be evaluated to generate a grid map corresponding to the model to be evaluated, wherein the grid map includes a plurality of boundary cells, and each boundary cell has a corresponding assigned point.

[0050] Partitioning the boundary of the model to be evaluated may involve dividing cells on the boundary of the domain. Boundary cells may be cells formed by meshing the surface of a three-dimensional model to be evaluated, or cells formed by meshing the envelope of a two-dimensional model to be evaluated.

[0051] For example, if Figure 3 As shown in FIG, a local grid diagram corresponding to a model to be evaluated provided in an embodiment of the present application is shown. In this example, the model to be evaluated is a three-dimensional model to be evaluated. Figure 3The grid diagram shown is the result of dividing part of the surface of the model to be evaluated, where Figure 3 Each triangular area in is a boundary unit, and the dot or circle in each boundary unit is the corresponding matching point of the boundary unit.

[0052] Step 203 : determining the strong interaction unit corresponding to each boundary unit according to the coordinates of the points of each boundary unit and the preset cutoff radius.

[0053] The preset cutoff radius can be determined based on the average unit area of all boundary units. It is understood that for a two-dimensional model diagram to be evaluated, the preset cutoff radius can be a circular radius; for a three-dimensional model to be evaluated, the preset cutoff radius can be a spherical radius. In actual use, the preset cutoff radius can also be selected according to other methods, and the embodiments of the present application are not limited to this.

[0054] For example, the preset cutoff radius can be based on Determine, where R is the preset cutoff radius, A i is the area of the i-th boundary unit in the grid map, mean(A i ) is the average area of all boundary element triangle meshes, and i is the sequence number of the boundary element in the mesh map.

[0055] It should be noted that the above examples are merely illustrative and cannot be regarded as limiting the present application. In actual use, the preset cutoff radius can be set according to actual needs and specific application scenarios, and the present application embodiment does not limit this.

[0056] It can be understood that based on the characteristic of the boundary element kernel function attenuating with distance, the amplitude of the three-dimensional acoustic boundary element kernel function decreases inversely with the increase of distance, that is, 1 / r; while the amplitude of the two-dimensional acoustic boundary element kernel function decreases inversely with the increase of the 1 / 2 power of the distance, that is, Among them, r is the distance between the boundary element source point and other matching points. That is to say, the farther the distance between the source point and other matching points, the weaker the interaction relationship between the source point and other matching points; conversely, the closer the distance between the source point and other matching points, the stronger the interaction relationship between the source point and other matching points. This relationship causes many elements in the system matrix to be non-zero but with very small amplitudes. Therefore, for each boundary element, a matching point is placed at the center of the element, and then the entire grid is divided into strong interaction elements and weak interaction elements around this matching point and the preset truncation radius.

[0057] As a possible implementation method, a kd-tree search algorithm can be used to traverse each boundary cell in the grid map corresponding to the model to be evaluated, and determine the strong interaction unit corresponding to each boundary cell based on the coordinates of the points of each boundary cell and the preset truncation radius. Among them, the kd tree (abbreviation of k-dimensional tree) is a data structure for organizing points in k-dimensional Euclidean space.

[0058] Furthermore, the boundary cells whose distance from the current source point is less than or equal to the preset cutoff radius can be determined as the strong interaction cells corresponding to the current source point. That is, in a possible implementation of the embodiment of the present application, the above step 203 may include:

[0059] Traverse the matching points of each boundary cell as the current source point;

[0060] When the distance between the distribution point of any boundary unit in the grid map and the current source point is less than or equal to the preset cutoff radius, the any boundary unit is determined as a strong interaction unit of the current source point.

[0061] As an example, see Figure 3 , with the current source point as the center, select the boundary unit whose distance to the current source point is less than or equal to the preset truncation radius. The boundary unit corresponding to the point is the strong interaction unit of the current source point. The boundary unit whose distance to the current source point is greater than the preset truncation radius is the weak interaction unit of the current source point.

[0062] Step 204 : Determine the sparse system matrix corresponding to the model to be evaluated based on each boundary cell and the strongly interacting cells corresponding to each boundary cell.

[0063] In an embodiment of the present application, for each boundary unit, since the interaction relationship between the boundary unit and its corresponding strong interaction unit is strong, and the correlation relationship between the boundary unit and its corresponding weak interaction unit is weak, the strong interaction unit can be selected to construct the system matrix corresponding to the model to be evaluated, and the weak interaction unit can be discarded. There is no need to calculate the weak interaction unit, thereby generating a sparse system matrix corresponding to the model to be evaluated. Compared with the traditional model reduction method orthogonal basis construction process in the prior art, the memory space and computational complexity required for the orthogonal basis construction process are reduced.

[0064] Furthermore, the sparse system matrix corresponding to the model to be evaluated may be determined by integration. That is, in a possible implementation of the embodiment of the present application, the above step 204 may include:

[0065] According to the distance between the collocation point of each boundary unit and the collocation point of each corresponding strongly interactive unit, the basic solution after the Taylor expansion is integrated to generate a sparse system matrix corresponding to the model to be evaluated.

[0066] Among them, the basic solution after Taylor expansion can be the basic solution of the two-dimensional or three-dimensional acoustic problem that is Taylor expanded to decouple the frequency domain and the spatial domain. The number of Taylor expansion terms is determined by the remaining terms, that is, it depends on the size of the model considered and the frequency range considered.

[0067] It should be understood that the sparse system matrix corresponding to the model to be evaluated is generated by integrating the fundamental solution after Taylor expansion. Therefore, the sparse system matrix corresponding to the model to be evaluated is frequency-independent. Therefore, the integral calculation only needs to be performed once within the frequency range under consideration, while the existing technology requires the integral calculation to be performed once for each frequency point of interest in the frequency domain of interest. Therefore, compared with the existing technology, the computational efficiency is improved.

[0068] It can be understood that by determining the sparse system matrix corresponding to the model to be evaluated based on the strongly interacting units corresponding to each boundary unit, the computational complexity of solving the system matrix is reduced. Only the sparse approximate matrix of the original large-scale dense matrix needs to be stored and solved, which reduces the memory requirements of the calculation process.

[0069] Step 205 : determining a global orthogonal basis of the sparse system matrix and the frequency extension points, wherein the frequency extension points are selected from the frequency domain of interest.

[0070] The frequency extension points can be pre-selected from the frequency domain of interest, automatically selected through an adaptive process, or user-defined based on actual usage requirements. In actual use, the number of frequency extension points can be selected based on actual needs, such as 3, 5, etc., and this embodiment of the application does not limit this.

[0071] As a possible implementation method, an orthogonal basis between the sparse system matrix and each frequency extension point may be first determined, and then a global orthogonal basis may be determined based on the local orthogonal basis between the sparse system matrix and each frequency extension point. That is, the above step 205 may include:

[0072] Determine a local orthogonal basis between each frequency extension point and the sparse system matrix;

[0073] Orthogonalize each local orthogonal basis to generate a global orthogonal basis.

[0074] The local orthogonal basis can be an orthogonal basis constructed by a frequency expansion point and a sparse system matrix. The orthogonal basis is the result of pairwise orthogonality of the elements. Then, orthogonalizing each local orthogonal basis can generate a global orthogonal basis.

[0075] As an example, a second-order Arnoldi method may be used to determine a local orthogonal basis for each frequency expansion point and a sparse system matrix, the dimension of which is determined by the condition number of the upper Hessenberg matrix.

[0076] It should be understood that the construction of the orthogonal basis is not limited to the second-order Arnoldi method, and the traditional Proper Orthogonal Decomposition (POD) method can also be used. The calculation principles are the same and will not be repeated here.

[0077] In one embodiment, orthogonalizing each local orthogonal basis to generate a global orthogonal basis may include:

[0078] Perform singular value decomposition on each local orthogonal basis to generate a global orthogonal basis.

[0079] Among them, singular value decomposition can decompose the features on any matrix.

[0080] Step 206 , taking the collocation point of each boundary unit as a source point in turn, integrating the basic solution after Taylor expansion according to the distance between the source point and the collocation point of each boundary unit in the grid map, to generate a column vector corresponding to each boundary unit.

[0081] It should be understood that the column vector corresponding to each boundary cell is generated by integrating the fundamental solution after Taylor expansion. Therefore, the column vector corresponding to each boundary cell is frequency-independent. Therefore, the integral calculation only needs to be performed once within the frequency range of interest, while the existing technology requires the integral calculation to be performed once for each frequency point of interest in the frequency domain of interest. Therefore, compared with the existing technology, the computational efficiency is improved.

[0082] Step 207 : Project the column vector corresponding to each boundary cell into the subspace spanned by the global orthogonal basis to generate a low-dimensional reduced-order model.

[0083] The column vector corresponding to each boundary unit may be projected into the subspace spanned by the global orthogonal basis in a column-by-column assembly projection manner.

[0084] It should be noted that the strong interaction unit corresponding to each boundary unit is determined by a preset cutoff radius, and then the sparse system matrix corresponding to the model to be evaluated is determined according to the strong interaction unit corresponding to each boundary unit. When solving the global orthogonal basis, it is no longer necessary to store and solve the original large-scale full-order model. Instead, it is necessary to store and solve the sparse approximate matrix of the original large-scale dense matrix, which reduces the memory requirements of the calculation process.

[0085] Furthermore, the above step 207 may include:

[0086] Traverse the source points of each boundary unit, and left-project the column vector formed by the source points of each boundary unit and all the matching points into the subspace spanned by the global orthogonal basis until all the source points of the boundary units are traversed; right-project the matrix obtained by left-projecting column by column into the subspace spanned by the global orthogonal basis to generate a low-dimensional reduced-order model.

[0087] As a possible implementation method, each time a column vector corresponding to a boundary element is generated, the column vector is left-projected into the subspace spanned by the global orthogonal basis; the points traversed through other boundary elements are returned as source points to integrate the points of all boundary elements in the grid diagram to generate a column vector corresponding to each boundary element, and then the column vector is left-projected into the subspace spanned by the global orthogonal basis; until the column vectors corresponding to all boundary elements are projected, all column vectors are left-projected to obtain a matrix, which is right-projected into the subspace spanned by the global orthogonal basis to generate a low-dimensional reduced-order model. The entire process does not form a dense matrix, which reduces memory requirements, and thus there is no need to store and calculate the original large-scale full-order model. The computational efficiency is also further improved, and larger-scale acoustic boundary element problems can be solved.

[0088] Step 208 : performing a frequency sweep calculation on the low-dimensional reduced-order model according to the frequency domain of interest, so as to generate a sound pressure evaluation result corresponding to the model to be evaluated in the frequency domain of interest.

[0089] The specific implementation process and principle of the above step 208 can be referred to the detailed description of the above embodiment and will not be repeated here.

[0090] In the above-mentioned sound pressure assessment method based on the model reduction boundary element method, the model to be evaluated and its corresponding frequency domain of interest are obtained, and a grid map corresponding to the model to be evaluated is generated. Then, according to the coordinates of the points of each boundary unit and the preset truncation radius, the strong interaction unit corresponding to each boundary unit is determined, and then the sparse system matrix corresponding to the model to be evaluated is constructed through the strong interaction unit, and then the local orthogonal basis of the sparse system matrix and the frequency expansion point is determined, and the global orthogonal basis is obtained by orthogonalizing all local orthogonal bases. The points of each boundary unit are used as source points to integrate the points of all boundary units in the grid map in turn to generate a column vector corresponding to each boundary unit, and then the column vector corresponding to each boundary unit is projected into the subspace spanned by the global orthogonal basis to generate a low-dimensional reduced-order model, and then the low-dimensional reduced-order model is subjected to frequency sweep calculation according to the frequency domain of interest to generate the sound pressure assessment result corresponding to the model to be evaluated in the frequency domain of interest. Therefore, through the preset truncation radius, the sparse system matrix corresponding to the model to be evaluated is constructed for the construction of the local orthogonal basis, and the second-order Arnoldi method is used to construct the local orthogonal basis. Compared with the orthogonal basis construction process of the traditional model order reduction method, the memory space and computational complexity required for constructing the orthogonal basis are reduced. Then, according to the constructed global orthogonal basis, the model to be evaluated is quickly reduced in order, the model to be evaluated is simplified, and the dimension of the system equation to be evaluated formed by the large-scale model to be evaluated is reduced, thereby reducing the memory requirement in the frequency sweeping process, improving the computational efficiency of the frequency sweeping analysis, and thus improving the efficiency of the sound pressure evaluation.

[0091] It should be understood that although Figure 1-2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1-2 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The order of execution of these steps or stages is not necessarily one by one, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0092] In one embodiment, Figure 4 As shown, a sound pressure assessment device based on the model-reduction boundary element method is provided. The device can be a software module or a hardware module, or a combination of the two to form a part of a computer device. The device specifically includes: an acquisition module 410, an order reduction module 420 and a frequency sweep module 430, wherein:

[0093] The acquisition module 410 is used to acquire the model to be evaluated and its corresponding frequency domain of interest.

[0094] The order reduction module 420 is used to perform order reduction processing on the model to be evaluated to generate a low-dimensional reduced-order model corresponding to the model to be evaluated.

[0095] The frequency sweep module 430 is configured to perform frequency sweep calculation on the low-dimensional reduced-order model according to the frequency domain of interest, so as to generate a sound pressure evaluation result corresponding to the model to be evaluated in the frequency domain of interest.

[0096] In one embodiment, the order reduction module 420 is further used to: divide the boundary of the model to be evaluated to generate a grid map corresponding to the model to be evaluated, wherein the grid map includes multiple boundary units, and each boundary unit has a corresponding distribution point; determine the strong interaction unit corresponding to each boundary unit according to the distribution point coordinates of each boundary unit and a preset truncation radius; determine the sparse system matrix corresponding to the model to be evaluated according to each boundary unit and the strong interaction unit corresponding to each boundary unit; determine the local orthogonal basis of the sparse system matrix and the frequency expansion point, and orthogonalize all local orthogonal bases to obtain a global orthogonal basis, wherein the frequency expansion point is selected from the frequency domain of interest; take the distribution point of each of the boundary units as the source point in turn, and integrate the basic solution after Taylor expansion according to the distance between the source point and the distribution point of each boundary unit in the grid map to generate a column vector corresponding to each boundary unit; project the column vector corresponding to each boundary unit into the subspace spanned by the global orthogonal basis to generate a low-dimensional reduced-order model.

[0097] In one embodiment, the order reduction module 420 is further used to: traverse the distribution points of each boundary unit as the current source point; when the distance between the distribution point of any boundary unit in the grid map and the current source point is less than or equal to a preset truncation radius, determine any boundary unit as a strong interaction unit of the current source point.

[0098] In one embodiment, the order reduction module 420 is further used to: integrate the basic solution after Taylor expansion according to the distance between the distribution point of each boundary unit and the distribution point of each corresponding strong interaction unit, to generate a sparse system matrix corresponding to the model to be evaluated.

[0099] In one embodiment, the order reduction module 420 is also used to: traverse the source points of each boundary unit, left-project the column vector formed by the source points of each boundary unit and all the matching points into the subspace spanned by the global orthogonal basis, until all the source points of the boundary units are traversed; right-project the matrix obtained by left projection column by column into the subspace spanned by the global orthogonal basis to generate a low-dimensional reduced-order model.

[0100] In one embodiment, the order reduction module 420 is further configured to: determine a local orthogonal basis between each frequency extension point and the sparse system matrix; and orthogonalize each local orthogonal basis to generate the global orthogonal basis.

[0101] In one embodiment, the order reduction module 420 is further configured to perform singular value decomposition on each local orthogonal basis to generate a global orthogonal basis.

[0102] The preset cutoff radius is determined according to the average area of all boundary cells.

[0103] Regarding the specific limitations of the sound pressure assessment device based on the model-reduced boundary element method, please refer to the limitations of the sound pressure assessment method based on the model-reduced boundary element method above, which will not be repeated here. The various modules in the above-mentioned sound pressure assessment device based on the model-reduced boundary element method can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0104] In one embodiment, Figure 5 This is a schematic diagram of the structure of the terminal device provided by this application. Figure 5 As shown, the terminal device 700 of this embodiment includes: at least one processor 710 ( Figure 5 Only one is shown in the figure) a processor, a memory 720, and a computer program 721 stored in the memory 720 and executable on the at least one processor 710, wherein the processor 710 implements the steps in the embodiment of the sound pressure assessment method based on the model reduction boundary element method when executing the computer program 721.

[0105] The terminal device 700 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device may include, but is not limited to, a processor 710 and a memory 720. Those skilled in the art will appreciate that Figure 5 This is merely an example of the terminal device 700 and does not constitute a limitation on the terminal device 700 . The terminal device 700 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device 700 may also include input and output devices, network access devices, etc.

[0106] The processor 710 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0107] In some embodiments, the memory 720 may be an internal storage unit of the terminal device 700, such as a hard disk or memory of the terminal device 700. In other embodiments, the memory 720 may also be an external storage device of the terminal device 700, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. equipped on the terminal device 700. Furthermore, the memory 720 may also include both an internal storage unit of the terminal device 700 and an external storage device. The memory 720 is used to store an operating system, application programs, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 720 may also be used to temporarily store data that has been output or is about to be output.

[0108] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0109] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0110] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0111] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0112] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0113] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0114] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0115] The present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed through a computer program product. When the computer program product is run on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing.

[0116] The above embodiments are intended only to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they may still modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application and should be included within the scope of protection of the present application.

Claims

1. A sound pressure assessment method based on model reduction boundary element method, characterized in that: include: Obtain the model to be evaluated and its corresponding frequency domain of interest; Performing order reduction processing on the model to be evaluated to generate a low-dimensional reduced-order model corresponding to the model to be evaluated; Performing a frequency sweep calculation on the low-dimensional reduced-order model according to the frequency domain of interest to generate a sound pressure evaluation result corresponding to the model to be evaluated in the frequency domain of interest; The step of performing order reduction processing on the model to be evaluated to generate a low-dimensional reduced-order model corresponding to the model to be evaluated includes: Dividing the boundary of the model to be evaluated to generate a grid map corresponding to the model to be evaluated, wherein the grid map includes a plurality of boundary units, and each boundary unit has a corresponding distribution point; Determining a strong interaction unit corresponding to each boundary unit according to the coordinates of the points of each boundary unit and a preset cutoff radius; Determine a sparse system matrix corresponding to the model to be evaluated according to each boundary unit and the strong interaction unit corresponding to each boundary unit; Determining a global orthogonal basis of the sparse system matrix and frequency extension points, wherein the frequency extension points are selected from the frequency domain of interest; Taking the collocation point of each boundary unit as a source point in turn, integrating the basic solution after Taylor expansion according to the distance between the source point and the collocation point of each boundary unit in the grid map to generate a column vector corresponding to each boundary unit; Projecting the column vector corresponding to each of the boundary cells into the subspace spanned by the global orthogonal basis to generate the low-dimensional reduced-order model; The step of determining the strong interaction unit corresponding to each boundary unit according to the coordinates of the points of each boundary unit and a preset cutoff radius includes: Traversing each of the boundary cells' assigned points as the current source point; When the distance between the distribution point of any boundary unit in the grid map and the current source point is less than or equal to a preset truncation radius, the any boundary unit is determined as a strong interaction unit of the current source point.

2. The sound pressure assessment method based on the model reduction boundary element method according to claim 1, characterized in that: The step of determining the sparse system matrix corresponding to the model to be evaluated according to each boundary unit and the strongly interacting unit corresponding to each boundary unit includes: According to the distance between the collocation point of each boundary unit and the collocation point of each corresponding strongly interactive unit, the basic solution after the Taylor expansion is integrated to generate a sparse system matrix corresponding to the model to be evaluated.

3. The sound pressure assessment method based on the model reduction boundary element method according to claim 1, characterized in that: The projecting of the column vector corresponding to each boundary cell into the subspace spanned by the global orthogonal basis to generate the low-dimensional reduced-order model includes: Traversing the source points of each boundary unit, and left-projecting the column vector formed by the source point of each boundary unit and all the collocation points into the subspace spanned by the global orthogonal basis until all the source points of the boundary units are traversed; The matrix obtained by left projection column by column is right-projected into the subspace spanned by the global orthogonal basis to generate a low-dimensional reduced-order model.

4. The sound pressure assessment method based on the model reduction boundary element method according to claim 1, characterized in that: The determining of the global orthogonal basis of the sparse system matrix and the frequency extension point comprises: determining a local orthogonal basis between each of the frequency extension points and the sparse system matrix; Orthogonalize each of the local orthogonal bases to generate the global orthogonal base.

5. The sound pressure assessment method based on the model reduction boundary element method according to claim 4, characterized in that: Orthogonalizing each of the local orthogonal bases to generate the global orthogonal base includes: Performing singular value decomposition on each of the local orthogonal bases to generate the global orthogonal base.

6. The sound pressure assessment method based on the model reduction boundary element method according to any one of claims 1 to 5, characterized in that: The preset cutoff radius is determined according to the average area of all boundary cells.

7. A sound pressure assessment device based on model reduction boundary element method, characterized in that: include: An acquisition module is used to obtain the model to be evaluated and its corresponding frequency domain of interest; An order reduction module is used to reduce the order of the model to be evaluated to generate a low-dimensional reduced-order model corresponding to the model to be evaluated; a frequency sweep module, configured to perform a frequency sweep calculation on the low-dimensional reduced-order model according to the frequency domain of interest, so as to generate a sound pressure evaluation result corresponding to the model to be evaluated in the frequency domain of interest; The order reduction module is further used to: divide the boundary of the model to be evaluated to generate a grid map corresponding to the model to be evaluated, wherein the grid map includes a plurality of boundary units, each of which has a corresponding distribution point; determine the strong interaction unit corresponding to each boundary unit according to the distribution point coordinates of each boundary unit and a preset truncation radius; determine the sparse system matrix corresponding to the model to be evaluated according to each boundary unit and the strong interaction unit corresponding to each boundary unit; determine the global orthogonal basis of the sparse system matrix and the frequency extension point, wherein the frequency extension point is selected from the frequency domain of interest; and sequentially The distribution point of each boundary unit is used as a source point, and the basic solution after Taylor expansion is integrated according to the distance between the source point and the distribution point of each boundary unit in the grid diagram to generate a column vector corresponding to each boundary unit; the column vector corresponding to each boundary unit is projected into the subspace spanned by the global orthogonal basis to generate the low-dimensional reduced-order model; and the distribution point of each boundary unit is traversed as the current source point; when the distance between the distribution point of any boundary unit in the grid diagram and the current source point is less than or equal to a preset truncation radius, the any boundary unit is determined as a strong interaction unit of the current source point.

8. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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