A simulation method, electronic device and storage medium for multi-speed motor noise

Abstract

The present application provides a simulation method, electronic device and storage medium for multi-speed motor noise, and relates to the field of simulation technology. The method comprises responding to a multi-speed motor noise analysis request for a target motor model, determining the speed quantity value and frequency point quantity value in a load file uploaded by a user, the degree of freedom and field point quantity value of an acoustic solution configured by the user, and the number of CPU parallel cores of an execution device; inputting the speed quantity value and frequency point quantity value, the degree of freedom and field point quantity value and the number of CPU parallel cores into a pre-trained classification model to obtain a classification result output by the classification model; selecting the optimal solution indicated by the classification result to perform multi-speed noise simulation, obtaining the corresponding noise simulation results at multiple speeds of the target motor model, and integrating the pre-processing, solver and post-processing of the motor multi-speed analysis to improve the environmental adaptability and computational efficiency of the multi-speed motor noise simulation.

Description

Technical Field

[0001] The present application relates to the field of simulation technology, and in particular to a simulation method, electronic device and storage medium for multi-speed motor noise. Background Art

[0002] The motor noise problem has become a common concern for manufacturers and a difficult problem that urgently needs to be solved. By accurately calculating and optimizing motor noise during the design phase, the development cycle can be significantly shortened and costs can be reduced. Motors usually operate within a wide speed range. However, a single motor simulation for a specific operating condition and a fixed speed cannot fully represent its vibration and noise performance. Therefore, performing electromagnetic vibration noise simulation under multi-speed conditions and providing users with the shortest solution is still an important challenge facing current motor noise calculations. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a simulation method, electronic device and storage medium for multi-speed motor noise, so as to improve the adaptability of the multi-speed motor noise simulation to the operating environment and improve the efficiency of the simulation calculation.

[0004] In a first aspect, the present invention provides a method for simulating multi-speed motor noise, the method comprising responding to a multi-speed motor noise analysis request for a target motor model, determining the speed quantity values ​​and frequency point quantity values ​​in a load file uploaded by a user, the degrees of freedom and field point quantity values ​​of an acoustic solution configured by the user, and the number of CPU parallel cores of an execution device; inputting the speed quantity values ​​and frequency point quantity values, the degrees of freedom and field point quantity values, and the number of CPU parallel cores into a pre-trained classification model, obtaining a classification result output by the classification model, the classification result being used to indicate an optimal solution corresponding to the target motor model; simulating and obtaining the noise simulation results corresponding to the target motor model at multiple speeds through the optimal solution indicated by the classification result.

[0005] In an optional embodiment, the noise simulation result includes the sound pressure values ​​corresponding to all field points of the target motor model, and also includes generating and outputting a noise waterfall diagram based on the sound pressure values ​​corresponding to all field points of the target motor model at multiple speeds.

[0006] In an optional embodiment, the solution includes an acoustic transfer vector solution and a fast solution, and the classification model is trained and generated in the following manner: a sample data set is generated, the sample data set includes multiple sample data, each sample data includes a rotational speed quantity value and a frequency point quantity value, a degree of freedom and a field point quantity value, and a CPU parallel core number; for each sample data, corresponding noise simulation is performed by using the acoustic transfer vector solution and the fast solution, and the running time of each noise simulation is determined; for each sample data, based on the corresponding running time of the acoustic transfer vector solution and the fast solution, the sample data is labeled with a category label; the labeled sample data is input into the original model in sequence for classification, so that it outputs the corresponding category label to obtain a classification model.

[0007] In an optional embodiment, it also includes separately acquiring structural grid data and acoustic grid data of the target motor model; for each target speed value, based on the grid mapping relationship between the structural grid and the acoustic grid, converting the vibration velocity value corresponding to the structural grid into the vibration velocity value corresponding to the acoustic grid through grid interpolation.

[0008] In an optional implementation, for each unit center of the acoustic grid, the corresponding vibration velocity value is obtained in the following manner:

[0009] ;

[0010] in, For structural grid nodes The corresponding vibration velocity value, is the center of the acoustic grid unit The corresponding vibration velocity value, For structural grid nodes With the acoustic grid cell center The straight-line distance between them.

[0011] In an optional embodiment, when the solution is an acoustic transfer vector solution, the noise simulation results corresponding to the target motor model at multiple speeds are obtained by simulating the following steps: the acoustic transfer vectors corresponding to the structural grid nodes and the acoustic grid units are determined by the boundary element method; for each target speed, the target speed is obtained by the following method The sound pressure at each field point :

[0012]

[0013] in, is the acoustic transfer vector, The target speed The vibration velocity value corresponding to the lower acoustic grid unit, is the angular frequency.

[0014] In an optional embodiment, when the solution is a fast solution, the noise simulation results corresponding to the target motor model at multiple speeds are obtained by simulating through the following steps: For each target speed, the noise simulation result at the target speed is determined in the following way: Solve the preset boundary integral equations corresponding to all frequency points to obtain the sound pressure of the field point.

[0015] In a second aspect, the present invention provides a device for simulating noise of a multi-speed motor, the device comprising:

[0016] A response module is used to respond to a multi-speed motor noise analysis request for a target motor model, determine the speed quantity value and frequency point quantity value in the load file uploaded by the user, the degree of freedom and field point quantity value of the acoustic solution configured by the user, and the number of CPU parallel cores of the execution device;

[0017] A classification module is used to input the speed quantity value and the frequency point quantity value, the degree of freedom and the field point quantity value and the number of CPU parallel cores into a pre-trained classification model to obtain the classification result output by the classification model, and the classification result is used to indicate the optimal solution corresponding to the target motor model;

[0018] The simulation module is used to simulate and obtain the corresponding noise simulation results of the target motor model at multiple speeds through the optimal solution indicated by the classification result.

[0019] In a third aspect, the present invention provides an electronic device comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for simulating multi-speed motor noise as described in any of the aforementioned embodiments.

[0020] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for simulating multi-speed motor noise in any of the aforementioned embodiments are executed.

[0021] The present application provides a simulation method, electronic device and storage medium for multi-speed motor noise, wherein the method includes responding to a multi-speed motor noise analysis request for a target motor model, determining the speed quantity value and frequency point quantity value in a load file uploaded by a user, the degree of freedom and field point quantity value of the acoustic solution configured by the user, and the number of CPU parallel cores of the execution device; inputting the speed quantity value and frequency point quantity value, the degree of freedom and field point quantity value and the number of CPU parallel cores into a pre-trained classification model, obtaining a classification result output by the classification model, and the classification result is used to indicate the optimal solution corresponding to the target motor model; selecting the optimal solution indicated by the classification result for simulation, and obtaining the corresponding noise simulation results at multiple speeds of the target motor model.

[0022] By selecting the solution through the classification model, the optimal solution is automatically selected, which can not only make up for the shortcomings of a single method in some scenarios, but also comprehensively improve the efficiency of multi-speed motor noise simulation. The pre-processing, solver and post-processing of motor multi-speed analysis can also be integrated. Pre-processing is used to identify each speed and each frequency in the multi-speed load. The solver is used to perform an integrated solution for multi-speed data types. Post-processing can be used to directly draw results such as field point waterfall charts. This integrated solution greatly reduces the steps of user operations and improves work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 A flowchart of the training steps of a classification model provided in an embodiment of the present application;

[0025] Figure 2 A flowchart of a method for simulating noise of a multi-speed motor provided in an embodiment of the present application;

[0026] Figure 3 A schematic diagram of the structure of a multi-speed motor noise simulation device provided in an embodiment of the present application;

[0027] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] First, the application scenario of the present application is described. The technical solution of the present application is applicable to motor noise simulation.

[0029] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0030] Embodiment 1

[0031] When faced with the problem of motor noise simulation under multi-speed conditions, the performance indicators that need to be considered when automatically selecting the optimal method include: solution accuracy, solution calculation time, and specific function requirements.

[0032] In one embodiment of the present application, two solutions are proposed, namely, an acoustic transfer vector solution and a fast solution. The solution results of the two solutions are almost the same and both meet the accuracy requirements. Therefore, the analysis of the solution calculation time is mainly focused here.

[0033] Figure 1 A flow chart of the training steps of a classification model provided in an embodiment of the present application. In order to automatically select the best solution and perform multi-speed motor noise simulation, a classification model is provided in this embodiment. Figure 1 As shown, the classification model can be trained and generated in the following way:

[0034] S10, generating a sample data set, the sample data set including a plurality of sample data, each sample data including a rotation speed quantity value and a frequency point quantity value, a degree of freedom and a field point quantity value and a CPU parallel core number.

[0035] There are many factors that affect the solution calculation time, including computer hardware, such as memory, number of cores, the amount of memory and parallel cores allocated at runtime, the size of the acoustic model and field point model, the speed and number of frequencies of multi-speed loads, etc.

[0036] Create a sample data set. Each sample includes the speed quantity value, frequency point quantity value, degree of freedom of the acoustic model, field point quantity value of the acoustic model, and number of CPU parallel cores in the load file.

[0037] Among them, the rotational speed quantity value and the frequency point quantity value in the load file can be determined based on the load file, the degree of freedom of the acoustic model and the field point quantity value of the acoustic model can be determined based on the created acoustic model, and the number of CPU parallel cores can be determined based on the properties of the computer that executes this step.

[0038] S12. For each sample data, corresponding noise simulation is performed using the acoustic transfer vector solution and the fast solution, and the running time of each noise simulation is determined.

[0039] Through a large number of numerical simulation experiments, the time required to solve the motor noise under multi-speed conditions using two solution schemes in different scenarios is statistically analyzed.

[0040] S13. For each sample data, based on the running time corresponding to the acoustic transfer vector solution and the fast solution, the sample data is labeled with a category label.

[0041] For each sample data, the solution with the shortest solution time is marked as the category label.

[0042] S14. Input the labeled sample data into the original model in sequence for classification, so that it outputs the corresponding category label to obtain a classification model.

[0043] Here, the support vector machine (SVM) model is selected as the original model. An optimal decision boundary or hyperplane is obtained by fitting for the binary classification task, and then a trained classification model is obtained.

[0044] Embodiment 2

[0045] In this embodiment of the present application, a simulation software is provided, which can automatically select the optimal solution from the two provided solutions according to the user's simulation environment and conditions, and efficiently complete the motor multi-speed noise simulation.

[0046] Figure 2 The following is a flow chart of a method for simulating noise of a multi-speed motor provided in an embodiment of the present application. Figure 2 As shown, the simulation software provided in the embodiment of the present application is used to execute the following method:

[0047] S20, responding to a multi-speed motor noise analysis request for a target motor model, determining the speed quantity value and frequency point quantity value in the load file uploaded by the user, the degree of freedom and field point quantity value of the acoustic solution configured by the user, and the number of CPU parallel cores of the execution device.

[0048] It is understood that the load file is obtained by the user through the structural simulation software. The acoustic model and the structural model can also be imported by the user or created by the simulation software. The number of CPU parallel cores can be configured by the user according to the actual computer properties.

[0049] The load file here is specifically a structural vibration load file, which at least includes the corresponding vibration velocity value on each structural grid node of the motor at multiple different speeds and each frequency point.

[0050] The graphical user interface provided by the simulation software may include a simulation control. After the user configures the simulation environment, the user may click the simulation control to generate a multi-speed motor noise analysis request and start executing this step.

[0051] S21, input the speed quantity value and frequency point quantity value, the degree of freedom and field point quantity value and the number of CPU parallel cores into a pre-trained classification model, and obtain the classification result output by the classification model, wherein the classification result is used to indicate a solution corresponding to the target motor model.

[0052] The classification model is used to determine the optimal solution with the least time consumption in the current simulation environment.

[0053] S22. Using the solution indicated by the classification result, simulate and obtain the noise simulation results corresponding to the target motor model at multiple speeds.

[0054] The noise simulation results are obtained by determining the optimal solution, which includes the sound pressure values ​​corresponding to all field points of the target motor model.

[0055] The present application provides a method for simulating multi-speed motor noise, which selects a solution through a classification model, determines the optimal solution for the operating environment, and thereby improves the efficiency of multi-speed motor noise simulation.

[0056] Furthermore, the simulation model provided in this embodiment may also perform the following steps:

[0057] S23. Generate and output a noise waterfall diagram based on the sound pressure values ​​corresponding to all field points of the multiple speed target motor models.

[0058] In step S23, a visualization result such as a noise waterfall diagram or a sound pressure cloud diagram may be generated. Generating a noise waterfall diagram or the like for a user may enable the user to understand the simulation result more intuitively, which is helpful for analyzing the simulation result.

[0059] Embodiment 3

[0060] It should be noted that since the data in the load file is obtained through the structural mesh simulation of the motor model, the velocity at the center of the acoustic mesh unit on the surface of the motor model needs to be used as a boundary condition when performing noise simulation. Therefore, the load of the structural node needs to be interpolated to the center of the acoustic mesh unit through the mesh mapping relationship between the structural mesh and the acoustic mesh, so as to be used as an acoustic boundary condition for noise simulation analysis.

[0061] Here, the structural grid data and acoustic grid data of the target motor model can be obtained respectively. The structural grid data includes the coordinates of multiple structural grid nodes. The acoustic grid data includes the coordinates of multiple acoustic grid unit centers.

[0062] For each target speed value, based on the grid mapping relationship between the structural grid and the acoustic grid, the vibration velocity value corresponding to the structural grid is converted into the vibration velocity value corresponding to the acoustic grid.

[0063] Here we can use the maximum distance interpolation algorithm. This algorithm calculates the value of the target point by weighted average based on the distance between the target point and the surrounding source data points and their weights. The closer the source data point is, the greater the weight is, and the farther the source data point is, the smaller the weight is. The maximum distance (distance refers to the Euclidean distance) refers to the maximum search distance from the center of the target grid cell. , search for the source grid within this radius, and also need to give the maximum number of influencing units . In radius The number of source grid nodes searched in is ,like , then take the closest one according to the distance The source grid nodes are used as the influence grids for calculating the target grid cells. , then the number of nodes affected is taken as Based on the given search radius , find the radius from the center of the target grid cell The closest to the ball source grids, where , and then use the distance to apply weighting to obtain the interpolation result at the center of the target unit.

[0064] Specifically, for the unit center of each acoustic grid, the corresponding vibration velocity value can be obtained in the following way:

[0065] ;

[0066] in, For structural grid nodes The corresponding vibration velocity value (can be obtained from the load file), is the center of the acoustic grid unit The corresponding vibration velocity value, For structural grid nodes With the acoustic grid cell center The straight-line distance between .

[0067] It is understandable that the structural grid nodes here , is the valid structural mesh node within the search range.

[0068] Embodiment 4

[0069] In one embodiment of the present application, an acoustic transfer vector solution is provided.

[0070] When the solution is the acoustic transfer vector solution, the simulation software can obtain the corresponding noise simulation results of the target motor model at multiple speeds through the following steps:

[0071] The acoustic transfer vectors corresponding to the structural grid nodes and the acoustic grid units are determined by the boundary element method. Here, the acoustic transfer vector method can be used to establish a linear relationship between the acoustic grid and the field point grid, and the acoustic transfer vectors of all frequency points at a rotation speed can be calculated. This method is suitable for small models and high rotation speeds, and can quickly obtain the acoustic results on the field point grid.

[0072] Specifically, for each target speed, the target speed is obtained by the following method: The sound pressure at each field point :

[0073]

[0074] in, is the acoustic transfer vector, The target speed The vibration velocity value corresponding to the lower acoustic grid unit, is the angular frequency.

[0075] The acoustic transfer vector is related to the following physical parameters: the position of the acoustic grid, the processing of the acoustic grid, such as adding damping, the position of the field point grid, the calculation frequency, and the physical parameters of the acoustic medium (velocity, density, etc.). Therefore, the acoustic transfer vector method is very suitable for multi-speed problems. However, when the frequency distribution of different speeds is inconsistent, in order to speed up the calculation, it is necessary to ensure that the frequency range is between the maximum and minimum frequencies, and then perform frequency interpolation on the acoustic transfer vector to quickly obtain the results at any frequency, but this may introduce certain interpolation errors. If the acoustic transfer vector is calculated at each frequency without interpolation, the acoustic transfer vector will lose its advantage and significantly increase the calculation cost.

[0076] Furthermore, in one embodiment of the present application, the acoustic transfer vector can also be expanded, not only to support the sound radiation problem with the boundary type of normal velocity consistent with the mainstream acoustic simulation software, but also to support the solution of the sound radiation problem with the boundary type of sound pressure.

[0077] Based on the boundary element method, the calculation formula of the acoustic transfer vector for known sound pressure boundaries and known normal velocity boundaries is given. The boundary integral equation is as follows:

[0078] ;

[0079] in, and are the two coefficient matrices calculated by the kernel function, and denote the acoustic pressure on the boundary (acoustic mesh) and the element normal velocity vector, respectively. is the source vector. Using the boundary integral equation again, we can find The sound pressure at is given by the following equation:

[0080] ;

[0081] in, and are the two coefficient matrices calculated by the kernel function, It represents the sound pressure at the field point. is the source term vector.

[0082] For the radiation problem (passive term, i.e. ), the acoustic transfer vector can be applied to quickly optimize the acoustic response of a fixed field point in a radiating structure with a fixed boundary geometry, that is, by modifying the velocity or pressure distribution on the boundary to optimize the acoustic response of the selected field point and a given frequency. Given, then the sound pressure on the boundary is , substituting into the above formula we can get:

[0083] ;

[0084] in, is the acoustic transfer vector when the normal velocity of the boundary is given. Similarly, if the sound pressure on the boundary is Given, the normal velocity on the boundary , we can get:

[0085] ;

[0086] in, is the acoustic transfer vector for a given case of defined boundary sound pressure.

[0087] Embodiment 5

[0088] For multi-speed situations, the acoustic transfer vector method of method 1 is usually selected. However, the operation steps of this method are relatively cumbersome, and for large models or high-frequency problems, the computational cost of calculating the acoustic transfer vector is quite high. In addition, after completing the acoustic simulation, if the user needs to calculate the results of other field points, it is necessary to start the noise simulation calculation from the beginning, which will waste a lot of time. In order to simplify the software operation process and realize the rapid simulation of large models, in one embodiment of the present application, a fast solution is provided.

[0089] When the solution is a fast solution, the following steps can be used to simulate and obtain the corresponding noise simulation results of the target motor model at multiple speeds:

[0090] For each target speed, the noise simulation results at all target speeds are determined in the following way: Solve the preset boundary integral equations corresponding to all frequency points to obtain the sound pressure at the field point.

[0091] The fast solution abandons the acoustic transfer vector method and directly performs noise simulation on the vibration results of all frequencies at all speeds. Then, efficient solution algorithms such as fast multipole and adaptive crossover approximation are used to quickly perform noise simulation calculations. For large models, its solution speed is more impressive than that of the acoustic transfer vector method.

[0092] When this solution is adopted, the software can provide a pop-up window to the user after the simulation is completed, asking whether the field point needs to be restarted. Since the acoustic boundary results of all frequency points at each speed are saved, when other field point results need to be solved, the field point restart function can be used. The simulation software will read the saved acoustic boundary results to quickly calculate the field point results without solving the linear equations again, which is very convenient for noise simulation analysis.

[0093] The core of the acoustic boundary element method is to correspond an original partial differential equation to a regional integral equation based on Green's function, and to convert it into a boundary integral equation from the point to be solved to the regional boundary through Gauss's theorem:

[0094] ;

[0095] in and Both are located at the border superior, is the sound pressure, .

[0096] In the above formula, the free space Green's function of the three-dimensional problem can be expressed as:

[0097] ;

[0098] in , If the observation point The area is smooth, is 0.5, otherwise For observation point The solid angle at is the sound pressure of the source field, which generally includes plane wave excitation, monopole, dipole and other sound sources. The algebraic equation after boundary element discretization is in the form of:

[0099] ;

[0100] In the formula and are respectively composed of the surface discrete sound pressure and its normal derivative dimensional column vector, For sound source related dimensional column vector. The discretized algebraic equation describes the linear relationship between the sound pressure, normal particle velocity, and sound source term at the discrete points on the boundary. After solving, the physical quantities of all discrete nodes on the boundary (i.e., sound pressure and normal velocity) can be obtained, and then the following boundary integral equation can be calculated:

[0101] :

[0102] The sound pressure and other physical quantities at any point in the entire sound field can be obtained.

[0103] Therefore, after the acoustic simulation is completed, the sound pressure and its normal derivative of the boundary unit can be saved in the result file. The next time the user wants to view acoustic physical quantities such as sound pressure and sound velocity at other field points, there is no need to re-solve the boundary integral equation. The software can directly read the boundary unit result information file and use the above boundary integral formula to calculate the acoustic physical quantities at the new field point. This method is called fast field point restart calculation.

[0104] Embodiment 6

[0105] Figure 3 The structure diagram of a multi-speed motor noise simulation device provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, based on the same inventive concept, the embodiment of the present application also provides a simulation device 30 for multi-speed motor noise, the device comprising:

[0106] A response module 310 is used to respond to a multi-speed motor noise analysis request for a target motor model, determine the speed quantity value and frequency point quantity value in the load file uploaded by the user, the degree of freedom and field point quantity value of the acoustic solution configured by the user, and the number of CPU parallel cores of the execution device;

[0107] The classification module 320 is used to input the speed quantity value and the frequency point quantity value, the degree of freedom and the field point quantity value and the number of CPU parallel cores into a pre-trained classification model to obtain a classification result output by the classification model, and the classification result is used to indicate the optimal solution corresponding to the target motor model;

[0108] The simulation module 330 is used to simulate and obtain the corresponding noise simulation results of the target motor model at multiple speeds through the optimal solution indicated by the classification result.

[0109] In a preferred embodiment, the noise simulation results include the sound pressure values ​​corresponding to all field points of the target motor model, and also include a visualization module (not shown in the figure) for generating and outputting a noise waterfall diagram based on the sound pressure values ​​corresponding to all field points of the target motor model at multiple speeds.

[0110] In a preferred embodiment, the solution includes an acoustic transfer vector solution and a fast solution, and also includes a training module (not shown in the figure), which is used to train and generate a classification model in the following manner: generate a sample data set, the sample data set includes multiple sample data, each sample data includes a rotation speed quantity value and a frequency point quantity value, a degree of freedom and a field point quantity value and a CPU parallel core number; for each sample data, perform corresponding noise simulation through the acoustic transfer vector solution and the fast solution, respectively, and determine the running time of each noise simulation; for each sample data, based on the size of the corresponding running time of the acoustic transfer vector solution and the fast solution, label the sample data with a category label; input the labeled sample data into the original model in turn for classification, so that it outputs the corresponding category label to obtain a classification model.

[0111] In a preferred embodiment, an interpolation module (not shown in the figure) is also included, which is used to respectively obtain the structural grid data and the acoustic grid data of the target motor model; for each target speed value, based on the grid mapping relationship between the structural grid and the acoustic grid, the vibration velocity value corresponding to the structural grid is converted into the vibration velocity value corresponding to the acoustic grid through grid interpolation.

[0112] In a preferred embodiment, the interpolation module obtains the corresponding vibration velocity value for each unit center of the acoustic grid by:

[0113] ;

[0114] in, For structural grid nodes The corresponding vibration velocity value, is the center of the acoustic grid unit The corresponding vibration velocity value, For structural grid nodes With the acoustic grid cell center The straight-line distance between them.

[0115] In a preferred embodiment, when the solution is an acoustic transfer vector solution, the simulation module 330 obtains the noise simulation results corresponding to the target motor model at multiple speeds by simulating the following steps: determining the acoustic transfer vectors corresponding to the structural grid nodes and the acoustic grid units by the boundary element method; for each target speed, obtaining the target speed by the following method: The sound pressure at each field point :

[0116]

[0117] in, is the acoustic transfer vector, The target speed The vibration velocity value corresponding to the lower acoustic grid unit, is the angular frequency.

[0118] In a preferred embodiment, when the solution is a fast solution, the simulation module 330 simulates and obtains the corresponding noise simulation results of the target motor model at multiple speeds through the following steps: For each target speed, the noise simulation result at the target speed is determined in the following way: Solve the preset boundary integral equations corresponding to all frequency points to obtain the sound pressure of the field point.

[0119] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 4 As shown in , the electronic device 400 includes a processor 410 , a memory 420 and a bus 430 .

[0120] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 is running, the processor 410 communicates with the memory 420 via the bus 430. When the machine-readable instructions are executed by the processor 410, the above-mentioned Figure 1 The steps of a method for simulating noise of a multi-speed motor in the method embodiment shown are specifically implemented in the method embodiment and will not be described in detail here.

[0121] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 The steps of a method for simulating noise of a multi-speed motor in the method embodiment shown are specifically implemented in the method embodiment and will not be described in detail here.

[0122] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0123] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, 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 communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0124] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0125] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0126] It should be noted that if the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0127] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0128] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for simulating noise of a multi-speed motor, characterized in that: The method comprises: Respond to the multi-speed motor noise analysis request for the target motor model, determine the speed quantity value and frequency point quantity value in the load file uploaded by the user, the degree of freedom and field point quantity value of the acoustic solution configured by the user, and the number of CPU parallel cores of the execution device; Inputting the rotation speed quantity value and the frequency point quantity value, the degree of freedom and the field point quantity value, and the number of CPU parallel cores into a pre-trained classification model, obtaining a classification result output by the classification model, wherein the classification result is used to indicate an optimal solution corresponding to a target motor model, wherein the optimal solution includes an acoustic transfer vector solution or a fast solution; By using the optimal solution indicated by the classification result, the noise simulation results corresponding to the target motor model at multiple speeds are obtained by simulation; When the solution is a fast solution, the noise simulation results corresponding to the target motor model at multiple speeds are obtained by simulation through the following steps: For each target speed, the noise simulation result at the target speed is determined by: Solve the preset boundary integral equations corresponding to all frequency points to obtain the sound pressure at the field point.

2. The method according to claim 1, characterized in that The noise simulation results include the sound pressure values ​​corresponding to all field points of the target motor model, and also include: Based on the sound pressure values ​​corresponding to all field points of the motor models with multiple speed targets, a noise waterfall diagram is generated and output.

3. The method according to claim 1, characterized in that: The solution includes an acoustic transfer vector solution and a fast solution, and the classification model is generated by training in the following manner: Generate a sample data set, wherein the sample data set includes a plurality of sample data, each sample data includes a rotation speed quantity value and a frequency point quantity value, a degree of freedom and a field point quantity value, and a CPU parallel core number; For each sample data, the corresponding noise simulation is performed by using the acoustic transfer vector solution and the fast solution, and the running time of each noise simulation is determined; For each sample data, the sample data is labeled with a category label based on the running time corresponding to the acoustic transfer vector solution and the fast solution; The labeled sample data are sequentially input into the original model for classification, so that it outputs corresponding category labels to obtain the classification model.

4. The method according to claim 1, characterized in that: Also includes: Respectively obtain the structural mesh data and acoustic mesh data of the target motor model; For each target speed value, based on the grid mapping relationship between the structural grid and the acoustic grid, the vibration velocity value corresponding to the structural grid is converted into the vibration velocity value corresponding to the acoustic grid through grid interpolation.

5. The method according to claim 4, characterized in that For each cell center of the acoustic grid, the corresponding vibration velocity value is obtained by: ； in, For structural grid nodes The corresponding vibration velocity value, is the center of the acoustic grid unit The corresponding vibration velocity value, For structural grid nodes With the acoustic grid cell center The straight-line distance between , is the maximum search radius from the center of the acoustic grid cell The number of structured grid nodes that can be searched within, The maximum number of nodes affected.

6. The method according to claim 1, characterized in that When the solution is an acoustic transfer vector solution, the noise simulation results corresponding to the target motor model at multiple speeds are obtained by simulation through the following steps: The corresponding acoustic transfer vectors between the structural grid nodes and the acoustic grid units are determined by the boundary element method; For each target speed, the target speed is obtained by the following method: The sound pressure at each field point : ； in, is the acoustic transfer vector, The target speed The vibration velocity value corresponding to the lower acoustic grid unit, is the angular frequency.

7. A multi-speed motor noise simulation device, characterized in that: The device comprises: A response module is used to respond to a multi-speed motor noise analysis request for a target motor model, determine the speed quantity value and frequency point quantity value in the load file uploaded by the user, the degree of freedom and field point quantity value of the acoustic solution configured by the user, and the number of CPU parallel cores of the execution device; A classification module, used for inputting the rotation speed quantity value and the frequency point quantity value, the degree of freedom and the field point quantity value and the number of CPU parallel cores into a pre-trained classification model, and obtaining a classification result output by the classification model, wherein the classification result is used for indicating an optimal solution corresponding to a target motor model, wherein the optimal solution includes an acoustic transfer vector solution or a fast solution; A simulation module is used to simulate and obtain the corresponding noise simulation results of the target motor model at multiple speeds through the optimal solution indicated by the classification result, wherein when the solution is a fast solution, the corresponding noise simulation results of the target motor model at multiple speeds are simulated and obtained through the following steps: for each target speed, the noise simulation result at the target speed is determined in the following way: solving the preset boundary integral equations corresponding to all frequency points to obtain the sound pressure of the field point.

8. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for simulating multi-speed motor noise as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for simulating noise of a multi-speed motor as claimed in any one of claims 1 to 6 are executed.

Images