A simulation experimental method for the sound insulation performance of acoustic metamaterials

Abstract

The present invention discloses a simulation experiment method for the sound insulation performance of an acoustic meta-material, comprising collecting environmental data and material data of a preset material, preprocessing the environmental data and the material data; using a finite element method to simulate the environmental data and the material data to obtain a simulated material, meshing the simulated material to obtain a partitioned grid; optimizing the partitioned grid according to a density threshold to obtain a key partitioned grid, performing multi-band verification and improvement according to the key partitioned grid to obtain a performance skewness; constructing a simulation experiment model for the sound insulation performance of a meta-material according to the performance skewness, optimizing the simulation experiment model for the sound insulation performance of the meta-material according to the simulation error, and outputting the simulation model. This method can not only improve the accuracy of the simulation experiment of the sound insulation performance of an acoustic meta-material, but also has good interpretability, and can be directly applied to a simulation experiment system for the sound insulation performance of an acoustic meta-material.

Description

Technical Field

[0001] The present invention relates to the field of performance simulation, and in particular to a simulation experimental method for the sound insulation performance of an acoustic metamaterial. Background Art

[0002] With the rapid development of science and technology, acoustic metamaterials, as a new type of material with special acoustic properties, have shown great application potential in the fields of sound insulation, sound absorption, sound wave guidance and acoustic stealth. However, the performance simulation and design of acoustic metamaterials has always been a complex and challenging topic. Traditional acoustic material design methods often rely on experimental trial and error, which is not only time-consuming and labor-intensive, but also difficult to fully explore the various possible properties of the material.

[0003] In the research and development of acoustic metamaterials, environmental conditions and material properties have a crucial impact on the performance of the final product. Environmental data, such as temperature, humidity, pressure, etc., can significantly change the acoustic properties of acoustic materials. At the same time, the physical and chemical properties of the material itself, such as density, elastic modulus, damping coefficient, etc., are also key factors in determining its acoustic performance. Therefore, accurately collecting and preprocessing these environmental data and material data is the basis for the simulation experiment of the sound insulation performance of acoustic metamaterials.

[0004] As a powerful numerical simulation method, the finite element method has been widely used in the simulation and analysis of acoustics. However, the accuracy and efficiency of finite element simulation are often restricted by the quality of meshing. Unreasonable meshing may lead to inaccurate simulation results, or even fail to reflect the true performance of acoustic metamaterials.

[0005] In addition, acoustic metamaterials usually need to show excellent performance in multiple frequency bands. Therefore, in the multi-band verification process, how to improve the material according to the simulation results to obtain a smaller performance deviation is also an important part of the acoustic metamaterial design.

[0006] In summary, in order to solve many problems in the simulation experiment of the sound insulation performance of acoustic metamaterials, an efficient and accurate simulation experiment method for the sound insulation performance of acoustic metamaterials is urgently needed. This method should be able to fully consider the influence of environmental data and material data, use the finite element method for fine simulation, and optimize the grid division according to the grid density. At the same time, the material performance is continuously improved during the multi-band verification process, and finally an accurate and reliable simulation experiment model of the sound insulation performance of metamaterials is constructed. Summary of the invention

[0007] The purpose of the present invention is to provide a simulation experimental method for the sound insulation performance of acoustic metamaterials.

[0008] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0009] The present invention comprises the following steps:

[0010] Collecting environmental data and material data of preset material work, and preprocessing the environmental data and the material data;

[0011] The finite element method is used to simulate the environmental data and the material data to obtain a simulated material, and the simulated material is meshed to obtain a mesh; the acoustic simulation includes the simulation of sound wave absorption, sound insulation performance, negative refraction and band gap characteristics; including:

[0012] The simulated material is randomly divided to obtain an initial grid, acoustic simulation is performed on the simulated material to obtain acoustic simulation data, and the grid density is gradually increased on the propagation path of the sound wave based on the change of the obtained acoustic simulation data; the acoustic simulation data includes sound pressure distribution data and sound intensity distribution data;

[0013] The single initial grid is divided into subgrids by gradient error, and the subgrids with the gradient error of acoustic simulation data lower than 0.201 are used as edge grids, and the subgrids with the gradient error lower than 0.201 are used as interval grids;

[0014] The edge grids and interval grids with grid density deviation less than 0.174 are classified into one category, otherwise they are classified as heterogeneous; the grids in one category are sorted in descending order according to grid density;

[0015] Output the classification results as a partitioned grid;

[0016] Optimizing and dividing the partitioned grid according to the density threshold to obtain a key partitioned grid, and performing multi-band verification and improvement according to the key partitioned grid to obtain a performance skewness;

[0017] A metamaterial sound insulation performance simulation experimental model is constructed according to the performance skewness, the metamaterial sound insulation performance simulation experimental model is optimized according to the simulation error, and a simulation model is output.

[0018] Furthermore, a method for obtaining a simulated material by simulating the environmental data and the material data using a finite element method includes:

[0019] Based on the finite element method, the collected environmental data and material data are used as input conditions, the simulation scene is set according to the environmental data, the simulated material is placed in the simulation scene, the material data is used to set the acoustic material parameters, and through discretization processing and numerical solution, the finite element model of the acoustic metamaterial is constructed to obtain the simulated material.

[0020] Furthermore, the method of optimizing the partitioning grid according to the density threshold to obtain the key partitioning grid includes:

[0021] Normalize the divided grids, traverse the divided grids, and add the material data in the divided grids to the corresponding grids;

[0022] Traverse all non-empty grids and adjacent grids and calculate the grid density:

[0023]

[0024]

[0025]

[0026]

[0027]

[0028] The i-th grid is , the zth grid is , Grid To Grid The external density of , Grid The intrinsic density is , Grid The intrinsic density is , Grid The linear sum of all material data coordinates in is , Grid To Grid The centroid distance is , Grid The linear sum of the coordinates of all material data in the uth dimension is , Grid The linear sum of the coordinates of all material data in the uth dimension is , the number of dimensions is , Grid The number of , the i-th material data is , the density of grid b is , the intrinsic density of grid b is , Grid The set of all neighboring grids of is , the neighborhood distance parameter is ;

[0029] Given a density threshold, when the mesh density is greater than or equal to the density threshold, the mesh is defined as a core mesh; when the mesh density is less than the density threshold, the mesh is defined as an edge mesh; a core mesh set is constructed based on the core mesh, and an edge mesh set is constructed based on the edge mesh;

[0030] Perform breadth-first search on the core grid set. When the grid distance is less than or equal to the neighborhood distance parameter, the two grids are classified into one category. If the distance between core grid A and core grid B is within the neighborhood distance parameter, and the distance between core grid B and core grid C is within the neighborhood distance parameter, core grid A and core grid C are classified into one category. Edge grids are classified into the category of core grids whose distance is within the neighborhood distance parameter. If there are multiple core grids within the neighborhood distance parameter of the edge grid, they are classified into the category of core grids with the largest grid density. If there is no core grid near the edge grid, it is removed.

[0031] Output the core grid set adjusted by breadth-first search as the key partitioning grid.

[0032] Furthermore, the method for improving the performance skewness by performing multi-band verification based on the key division grid includes:

[0033] Acquire the acoustic simulation data and experimental data of the key partitioned grids, and calculate the performance skewness of the key partitioned grids and experimental data in the set frequency band:

[0034]

[0035] The frequency band The next y-th performance skewness is , frequency band The next j-th acoustic simulation data is , frequency band The next j-th experimental data is , the number of acoustic simulation data included in the yth performance is , the adjustment constant is , the control coefficient is , frequency band The average value of the acoustic simulation data is , frequency band The average value of the experimental data is ;

[0036] Introducing particle population, taking the minimum performance skewness as the search strategy, searching for the control coefficient according to the search strategy;

[0037] Calculate the position of the particle:

[0038]

[0039] Among them The position of a particle in the wth dimension is , the target location is , the step size factor is , the maximum dimension is , the minimum dimension is , the number of dimensions is , the walk parameter of the wth dimension is , the initial position of the particle is ;

[0040] Use the inertia weight to update the particle position to obtain the inertial position. The expression is:

[0041]

[0042] The t+1th iteration The inertial position of a particle in the wth dimension is , the tth iteration The position of a particle in the wth dimension is , the inertia weight of the tth iteration is , a random number from 0 to 1 is , the maximum number of iterations is , the current number of iterations is t;

[0043] Update the inertia weight, the expression is:

[0044]

[0045] The inertia weight of the t+1th iteration is , the maximum value of inertia weight is , the minimum value of inertia weight is ;

[0046] Iterate continuously until the minimum deviation is reached and output the performance skewness, otherwise update the inertia weight.

[0047] Furthermore, a method for constructing a simulation experimental model of the sound insulation performance of a metamaterial according to the performance skewness includes:

[0048] The performance skewness and loss function are weighted to construct the objective function of the experimental model for simulating the sound insulation performance of metamaterials;

[0049] The experimental model for simulating the sound insulation performance of metamaterials uses random forest algorithm, finite difference time domain method, boundary element method and neural network algorithm;

[0050] The random forest algorithm divides the input data into training and test sets by voting;

[0051] The finite-difference time-domain method discretizes the continuous time and space based on the training set, applies the wave equation at discrete points, simulates the propagation process of sound waves in acoustic metamaterials, and obtains simulation data;

[0052] The boundary element method discretizes the unknown quantities on the boundary in the simulation data and solves the boundary integral equation to obtain the acoustic performance;

[0053] The neural network algorithm builds a neural network and iteratively trains the acoustic performance of the acoustic metamaterial band gap characteristics under different parameters to obtain the mapping relationship between the parameters and the band gap characteristics, and predicts the performance of the acoustic metamaterial based on the mapping relationship.

[0054] Furthermore, the method for optimizing the experimental model for simulating the sound insulation performance of the metamaterial according to the simulation error includes:

[0055] An optimization algorithm is introduced, the variance of simulation data and experimental data is taken as simulation error, the simulation error is taken as fitness function, the particle fitness is calculated, and the particle position with the minimum fitness is taken as the optimal position;

[0056] The chaotic mapping of particle population is expressed as:

[0057]

[0058] The chaotic random number from 0 to 1 is , No. The dimensions are , No. The dimensions are , a random number from 0 to 1 is ;

[0059] Update the particle's position, the expression is:

[0060]

[0061] The lower bound of the w-th dimension is , the upper bound of the w-th dimension is , a random number from 0 to 1 is , the random number from 1 to 2 is , the updated position of the ath particle in the wth dimension at the t+1th iteration is , the starting position of the ath particle in the wth dimension at the tth iteration is , the optimal position of the particle in the tth iteration is ;

[0062] By adjusting the search method to update the particle position, the attack position is obtained, and the expression is:

[0063]

[0064] The control parameters are , a random number from 0 to 1 is , the current number of iterations is t, and the maximum number of iterations is , the attack position of the ath particle in the wth dimension at the t+1th iteration is , the constant is ;

[0065] Update the particle position to obtain the migration position. The expression is:

[0066]

[0067] The fitness of the ath particle is , the fitness of random particles is , the attack position of the ath particle in the wth dimension at the tth iteration is , the migration position of the ath particle in the wth dimension at the t+1th iteration is , a random number from 0 to 1 is , the Cauchy mutation becomes ;

[0068] The probability density function of the Cauchy mutation is expressed as:

[0069]

[0070] The independent variable is m and the position parameter is , the scale parameter is , the probability density function is ;

[0071] Update the particle position according to the convergence factor to obtain the converged position. The expression is:

[0072]

[0073] The interference factor is , the influence constant is , the migration position of the ath particle in the wth dimension at the tth iteration is , the convergence position of the ath particle in the wth dimension at the t+1th iteration is ;

[0074] Continue iterating until the fitness is minimized and then stop iterating.

[0075] In a second aspect, an embodiment of the present application further provides an electronic device, including:

[0076] A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0077] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0078] The beneficial effects of the present invention are:

[0079] The present invention is a simulation experimental method for the sound insulation performance of an acoustic metamaterial. Compared with the prior art, the present invention has the following technical effects:

[0080] The present invention can improve the accuracy of the simulation experiment of the sound insulation performance of the acoustic metamaterial through preprocessing, material simulation, grid division, optimization division, multi-band verification improvement, model construction and model optimization steps, thereby improving the accuracy of the simulation experiment of the sound insulation performance of the acoustic metamaterial, optimizing the simulation experiment of the sound insulation performance of the acoustic metamaterial, greatly saving resources, and improving work efficiency, and can realize intelligent simulation of the performance of the acoustic metamaterial, and can perform grid division and verification improvement on the simulation experiment of the sound insulation performance of the acoustic metamaterial in real time, which is of great significance to the simulation experiment of the sound insulation performance of the acoustic metamaterial, can adapt to the simulation experiment of the sound insulation performance of acoustic metamaterials of different standards and the simulation experiment requirements of the sound insulation performance of different acoustic metamaterials, and has certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A flowchart of the steps of a simulation experimental method for the sound insulation performance of an acoustic metamaterial according to the present invention;

[0082] Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0083] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0084] The present invention provides a simulation experimental method for the sound insulation performance of an acoustic metamaterial, comprising the following steps:

[0085] like Figure 1 As shown, in this embodiment, the following steps are included:

[0086] Collecting environmental data and material data of preset material work, and preprocessing the environmental data and the material data;

[0087] In the actual evaluation, an acoustic metamaterial designed for sound wave guiding equipment was used as the research object; the environmental data was an outdoor square environment with a temperature of 30°C, a humidity of 35% RH, an environmental pressure of 100.8kPa, and an incident sound wave angle of 3°; the material data was a density of 4500kg / m³, an elastic modulus of 8GPa, a damping coefficient of 0.03, a Poisson's ratio of 0.3, a thickness of 0.5mm, a size of 100mm×100mm, an equivalent mass density of 5000kg / m³, and a bulk elastic modulus of 10GPa; the frequency range and sound pressure level of the added noise were 630Hz and 77dB respectively;

[0088] The finite element method is used to simulate the environmental data and the material data to obtain a simulated material, and the simulated material is meshed to obtain a mesh; the acoustic simulation includes the simulation of sound wave absorption, sound insulation performance, negative refraction and band gap characteristics; including:

[0089] The simulated material is randomly divided to obtain an initial grid, acoustic simulation is performed on the simulated material to obtain acoustic simulation data, and the grid density is gradually increased on the propagation path of the sound wave based on the change of the obtained acoustic simulation data; the acoustic simulation data includes sound pressure distribution data and sound intensity distribution data;

[0090] The single initial grid is divided into subgrids by gradient error, and the subgrids with the gradient error of acoustic simulation data lower than 0.201 are used as edge grids, and the subgrids with the gradient error lower than 0.201 are used as interval grids;

[0091] The edge grids and interval grids with grid density deviation less than 0.174 are classified into one category, otherwise they are classified as heterogeneous; the grids in one category are sorted in descending order according to grid density;

[0092] Output the classification results as a partitioned grid;

[0093] In the actual evaluation, based on the finite element method, a circular outdoor square simulation scene with a radius of 20m was set according to the environmental data, the simulation materials were arranged at the key parts of the sound wave guiding equipment, the acoustic material parameters were set according to the material data, and the simulation materials were obtained through discretization processing and numerical solution;

[0094] The grid size is 5mm×5mm, and the number of grids is 5000;

[0095] Optimizing and dividing the partitioned grid according to the density threshold to obtain a key partitioned grid, and performing multi-band verification and improvement according to the key partitioned grid to obtain a performance skewness;

[0096] In the actual evaluation, the density threshold is 0.73, the number of core grids is 3000, and the number of edge grids is 2000; after classification, the number of key division grids is 4000;

[0097] The performance skewness calculation result is 0.1, the control coefficient is 0.5, the adjustment constant is 0.05, the maximum value of the inertia weight is 0.9, and the minimum value is 0.4;

[0098] A metamaterial sound insulation performance simulation experimental model is constructed according to the performance skewness, the metamaterial sound insulation performance simulation experimental model is optimized according to the simulation error, and a simulation model is output.

[0099] In this embodiment, the method of obtaining the simulated material by simulating the environmental data and the material data using the finite element method includes:

[0100] Based on the finite element method, the collected environmental data and material data are used as input conditions, the simulation scene is set according to the environmental data, the simulated material is placed in the simulation scene, the material data is used to set the acoustic material parameters, and through discretization processing and numerical solution, the finite element model of the acoustic metamaterial is constructed to obtain the simulated material.

[0101] In this embodiment, the method of optimizing the partitioning grids according to the density threshold to obtain the key partitioning grids includes:

[0102] Normalize the divided grids, traverse the divided grids, and add the material data in the divided grids to the corresponding grids;

[0103] Traverse all non-empty grids and adjacent grids and calculate the grid density:

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] The i-th grid is , the zth grid is , Grid To Grid The external density of , Grid The intrinsic density is , Grid The intrinsic density is , Grid The linear sum of all material data coordinates in is , Grid To Grid The centroid distance is , Grid The linear sum of the coordinates of all material data in the uth dimension is , Grid The linear sum of the coordinates of all material data in the uth dimension is , the number of dimensions is , Grid The number of , the i-th material data is , the density of grid b is , the intrinsic density of grid b is , Grid The set of all neighboring grids of is , the neighborhood distance parameter is ;

[0110] Given a density threshold, when the mesh density is greater than or equal to the density threshold, the mesh is defined as a core mesh; when the mesh density is less than the density threshold, the mesh is defined as an edge mesh; a core mesh set is constructed based on the core mesh, and an edge mesh set is constructed based on the edge mesh;

[0111] Perform breadth-first search on the core grid set. When the grid distance is less than or equal to the neighborhood distance parameter, the two grids are classified into one category. If the distance between core grid A and core grid B is within the neighborhood distance parameter, and the distance between core grid B and core grid C is within the neighborhood distance parameter, core grid A and core grid C are classified into one category. Edge grids are classified into the category of core grids whose distance is within the neighborhood distance parameter. If there are multiple core grids within the neighborhood distance parameter of the edge grid, they are classified into the category of core grids with the largest grid density. If there is no core grid near the edge grid, it is removed.

[0112] Outputting the core grid set adjusted by the breadth-first search as a key partitioning grid;

[0113] In the actual evaluation, the neighborhood distance parameter ϑ = 0.15m is set to calculate the grid density; the density threshold is set to 0.751, the core grid and the edge grid are distinguished, and the corresponding grid set is constructed. The core grid set is searched for classification grids with breadth first priority, the edge grids are processed, and the key partition grids are output;

[0114] Acquire acoustic simulation and experimental data in the frequency range of 500 - 10000 Hz and set adjustment constants = 0.02, control coefficient = 0.4; the performance skewness is 0.114.

[0115] In this embodiment, the method for improving the performance skewness by performing multi-band verification according to the key division grid includes:

[0116] Acquire the acoustic simulation data and experimental data of the key partitioned grids, and calculate the performance skewness of the key partitioned grids and experimental data in the set frequency band:

[0117]

[0118] The frequency band The next y-th performance skewness is , frequency band The next j-th acoustic simulation data is , frequency band The next j-th experimental data is , the number of acoustic simulation data included in the yth performance is , the adjustment constant is , the control coefficient is , frequency band The average value of the acoustic simulation data is , frequency band The average value of the experimental data is ;

[0119] Introducing particle population, taking the minimum performance skewness as the search strategy, searching for the control coefficient according to the search strategy;

[0120] Calculate the position of the particle:

[0121]

[0122] Among them The position of a particle in the wth dimension is , the target location is , the step size factor is , the maximum dimension is , the minimum dimension is , the number of dimensions is , the walk parameter of the wth dimension is , the initial position of the particle is ;

[0123] Use the inertia weight to update the particle position to obtain the inertial position. The expression is:

[0124]

[0125] The t+1th iteration The inertial position of a particle in the wth dimension is , the tth iteration The position of a particle in the wth dimension is , the inertia weight of the tth iteration is , a random number from 0 to 1 is , the maximum number of iterations is , the current number of iterations is t;

[0126] Update the inertia weight, the expression is:

[0127]

[0128] The inertia weight of the t+1th iteration is , the maximum value of inertia weight is , the minimum value of inertia weight is ;

[0129] Iterate continuously until the minimum deviation is reached and output the performance skewness, otherwise update the inertia weight.

[0130] In this embodiment, a method for constructing a simulation experimental model of the sound insulation performance of a metamaterial according to the performance skewness includes:

[0131] The performance skewness and loss function are weighted to construct the objective function of the experimental model for simulating the sound insulation performance of metamaterials;

[0132] The experimental model for simulating the sound insulation performance of metamaterials uses random forest algorithm, finite difference time domain method, boundary element method and neural network algorithm;

[0133] The random forest algorithm divides the input data into training and test sets by voting;

[0134] The finite-difference time-domain method discretizes the continuous time and space based on the training set, applies the wave equation at discrete points, simulates the propagation process of sound waves in acoustic metamaterials, and obtains simulation data;

[0135] The boundary element method discretizes the unknown quantities on the boundary in the simulation data and solves the boundary integral equation to obtain the acoustic performance;

[0136] The neural network algorithm builds a neural network and iteratively trains the acoustic performance of the acoustic metamaterial band gap characteristics under different parameters to obtain the mapping relationship between the parameters and the band gap characteristics, and predicts the performance of the acoustic metamaterial based on the mapping relationship.

[0137] In this embodiment, the method for optimizing the metamaterial sound insulation performance simulation experimental model according to the simulation error includes:

[0138] An optimization algorithm is introduced, the variance of simulation data and experimental data is taken as simulation error, the simulation error is taken as fitness function, the particle fitness is calculated, and the particle position with the minimum fitness is taken as the optimal position;

[0139] The chaotic mapping of particle population is expressed as:

[0140]

[0141] The chaotic random number from 0 to 1 is , No. The dimensions are , No. The dimensions are , a random number from 0 to 1 is ;

[0142] Update the particle's position, the expression is:

[0143]

[0144] The lower bound of the w-th dimension is , the upper bound of the w-th dimension is , a random number from 0 to 1 is , the random number from 1 to 2 is , the updated position of the ath particle in the wth dimension at the t+1th iteration is , the starting position of the ath particle in the wth dimension at the tth iteration is , the optimal position of the particle in the tth iteration is ;

[0145] By adjusting the search method to update the particle position, the attack position is obtained, and the expression is:

[0146]

[0147] The control parameters are , a random number from 0 to 1 is , the current number of iterations is t, and the maximum number of iterations is , the attack position of the ath particle in the wth dimension at the t+1th iteration is , the constant is ;

[0148] Update the particle position to obtain the migration position. The expression is:

[0149]

[0150] The fitness of the ath particle is , the fitness of random particles is , the attack position of the ath particle in the wth dimension at the tth iteration is , the migration position of the ath particle in the wth dimension at the t+1th iteration is , a random number from 0 to 1 is , the Cauchy mutation becomes ;

[0151] The probability density function of the Cauchy mutation is expressed as:

[0152]

[0153] The independent variable is m and the position parameter is , the scale parameter is , the probability density function is ;

[0154] Update the particle position according to the convergence factor to obtain the converged position. The expression is:

[0155]

[0156] The interference factor is , the influence constant is , the migration position of the ath particle in the wth dimension at the tth iteration is , the convergence position of the ath particle in the wth dimension at the t+1th iteration is ;

[0157] Continue iterating until the fitness is minimized and then stop iterating.

[0158] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0159] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0160] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0161] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a simulation experimental device for the sound insulation performance of acoustic metamaterials at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the above-mentioned simulation experimental methods for the sound insulation performance of acoustic metamaterials.

[0162] The above application Figure 1 The simulation experimental method of the sound insulation performance of an acoustic metamaterial disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0163] The electronic device may also perform Figure 1 A simulation experimental method for the sound insulation performance of acoustic metamaterials was implemented Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0164] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple application programs, any one of the aforementioned simulation experimental methods for the sound insulation performance of acoustic metamaterials is executed.

[0165] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0166] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0167] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0169] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0170] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0171] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0172] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0173] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0174] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A simulation experimental method for the sound insulation performance of acoustic metamaterials, characterized in that: The following steps are involved: Collecting environmental data and material data of preset material work, and preprocessing the environmental data and the material data; Using the finite element method to simulate the environmental data and the material data to obtain a simulated material, and meshing the simulated material to obtain a mesh; acoustics The simulation includes acoustic absorption, sound insulation, negative refraction and bandgap characteristics; including: The simulated material is randomly divided to obtain an initial grid, acoustic simulation is performed on the simulated material to obtain acoustic simulation data, and the grid density is gradually increased on the propagation path of the sound wave based on the change of the obtained acoustic simulation data; the acoustic simulation data includes sound pressure distribution data and sound intensity distribution data; The single initial grid is divided into subgrids by gradient error, and the subgrids with the gradient error of acoustic simulation data lower than 0.201 are used as edge grids, and the subgrids with the gradient error lower than 0.201 are used as interval grids; The edge grids and interval grids with grid density deviation less than 0.174 are classified into one category, otherwise they are classified as heterogeneous; the grids in one category are sorted in descending order according to grid density; Output the classification results as a partitioned grid; Optimizing and dividing the partitioned grid according to the density threshold to obtain a key partitioned grid, and performing multi-band verification and improvement according to the key partitioned grid to obtain a performance skewness; A metamaterial sound insulation performance simulation experimental model is constructed according to the performance skewness, the metamaterial sound insulation performance simulation experimental model is optimized according to the simulation error, and a simulation model is output.

2. The method for simulating the sound insulation performance of an acoustic metamaterial according to claim 1, characterized in that: The method of obtaining a simulated material by simulating the environmental data and the material data using the finite element method comprises: Based on the finite element method, the collected environmental data and material data are used as input conditions, the simulation scene is set according to the environmental data, the simulated material is placed in the simulation scene, the material data is used to set the acoustic material parameters, and through discretization processing and numerical solution, the finite element model of the acoustic metamaterial is constructed to obtain the simulated material.

3. The method for simulating the sound insulation performance of an acoustic metamaterial according to claim 1, characterized in that: The method of optimizing the partitioning grid according to the density threshold to obtain the key partitioning grid includes: Normalize the divided grids, traverse the divided grids, and add the material data in the divided grids to the corresponding grids; Traverse all non-empty grids and adjacent grids and calculate the grid density: The i-th grid is , the zth grid is , Grid To Grid The external density of , Grid The intrinsic density is , Grid The intrinsic density is , Grid The linear sum of all material data coordinates in is , Grid To Grid The centroid distance is , Grid The linear sum of the coordinates of all material data in the uth dimension is , Grid The linear sum of the coordinates of all material data in the uth dimension is , the number of dimensions is , Grid The number of , the i-th material data is , the density of grid b is , the intrinsic density of grid b is , Grid The set of all neighboring grids of is , the neighborhood distance parameter is ; Given a density threshold, when the mesh density is greater than or equal to the density threshold, the mesh is defined as a core mesh; when the mesh density is less than the density threshold, the mesh is defined as an edge mesh; a core mesh set is constructed based on the core mesh, and an edge mesh set is constructed based on the edge mesh; Perform breadth-first search on the core grid set. When the grid distance is less than or equal to the neighborhood distance parameter, the two grids are classified into one category. If the distance between core grid A and core grid B is within the neighborhood distance parameter, and the distance between core grid B and core grid C is within the neighborhood distance parameter, core grid A and core grid C are classified into one category. Edge grids are classified into the category of core grids whose distance is within the neighborhood distance parameter. If there are multiple core grids within the neighborhood distance parameter of the edge grid, they are classified into the category of core grids with the largest grid density. If there is no core grid near the edge grid, it is removed. Output the core grid set adjusted by breadth-first search as the key partitioning grid.

4. The method for simulating the sound insulation performance of an acoustic metamaterial according to claim 1, characterized in that: The method for improving the performance skewness by performing multi-band verification according to the key division grid includes: Acquire the acoustic simulation data and experimental data of the key partitioned grids, and calculate the performance skewness of the key partitioned grids and experimental data in the set frequency band: The frequency band The next y-th performance skewness is , frequency band The j-th acoustic simulation data is , frequency band The next j-th experimental data is , the number of acoustic simulation data included in the yth performance is , the adjustment constant is , the control coefficient is , frequency band The average value of the acoustic simulation data is , frequency band The average value of the experimental data is ; Introducing particle population, taking the minimum performance skewness as the search strategy, searching for the control coefficient according to the search strategy; Calculate the position of the particle: Among them The position of a particle in the wth dimension is , the target location is , the step size factor is , the maximum dimension is , the minimum dimension is , the number of dimensions is , the walk parameter of the wth dimension is , the initial position of the particle is ; Use the inertia weight to update the particle position to obtain the inertial position. The expression is: The t+1th iteration The inertial position of a particle in the wth dimension is , the tth iteration The position of a particle in the wth dimension is , the inertia weight of the tth iteration is , a random number from 0 to 1 is , the maximum number of iterations is , the current number of iterations is t; Update the inertia weight, the expression is: The inertia weight of the t+1th iteration is , the maximum value of inertia weight is , the minimum value of inertia weight is ; Iterate continuously until the minimum deviation is reached and output the performance skewness, otherwise update the inertia weight.

5. The method for simulating the sound insulation performance of an acoustic metamaterial according to claim 1, characterized in that: The method for constructing a simulation experimental model of the sound insulation performance of a metamaterial according to the performance skewness comprises: The performance skewness and loss function are weighted to construct the objective function of the experimental model for simulating the sound insulation performance of metamaterials; The experimental model for simulating the sound insulation performance of metamaterials uses random forest algorithm, finite difference time domain method, boundary element method and neural network algorithm; The random forest algorithm divides the input data into training and test sets by voting; The finite-difference time-domain method discretizes the continuous time and space based on the training set, applies the wave equation at discrete points, simulates the propagation process of sound waves in acoustic metamaterials, and obtains simulation data; The boundary element method discretizes the unknown quantities on the boundary in the simulation data and solves the boundary integral equation to obtain the acoustic performance; The neural network algorithm builds a neural network and iteratively trains the acoustic performance of the acoustic metamaterial band gap characteristics under different parameters to obtain the mapping relationship between the parameters and the band gap characteristics, and predicts the performance of the acoustic metamaterial based on the mapping relationship.

6. The method for simulating the sound insulation performance of an acoustic metamaterial according to claim 1, characterized in that: The method for optimizing the experimental model for simulating the sound insulation performance of the metamaterial according to the simulation error comprises: An optimization algorithm is introduced, the variance of simulation data and experimental data is taken as simulation error, the simulation error is taken as fitness function, the particle fitness is calculated, and the particle position with the minimum fitness is taken as the optimal position; The chaotic mapping of particle population is expressed as: The chaotic random number from 0 to 1 is , No. The dimensions are , No. The dimensions are , a random number from 0 to 1 is ; Update the particle's position, the expression is: The lower bound of the w-th dimension is , the upper bound of the w-th dimension is , a random number from 0 to 1 is , the random number from 1 to 2 is , the updated position of the ath particle in the wth dimension at the t+1th iteration is , the starting position of the ath particle in the wth dimension at the tth iteration is , the optimal position of the particle in the tth iteration is ; By adjusting the search method to update the particle position, the attack position is obtained, and the expression is: The control parameters are , a random number from 0 to 1 is , the current number of iterations is t, and the maximum number of iterations is , the attack position of the ath particle in the wth dimension at the t+1th iteration is , the constant is ; Update the particle position to obtain the migration position. The expression is: The fitness of the ath particle is , the fitness of random particles is , the attack position of the ath particle in the wth dimension at the tth iteration is , the migration position of the ath particle in the wth dimension at the t+1th iteration is , a random number from 0 to 1 is , the Cauchy mutation becomes ; The probability density function of the Cauchy mutation is expressed as: The independent variable is m and the position parameter is , the scale parameter is , the probability density function is ; Update the particle position according to the convergence factor to obtain the converged position. The expression is: The interference factor is , the influence constant is , the migration position of the ath particle in the wth dimension at the tth iteration is , the convergence position of the ath particle in the wth dimension at the t+1th iteration is ; Continue iterating until the fitness is minimized and then stop iterating.

7. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the method according to any one of claims 1 to 6.

8. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, enables the electronic device to execute the method according to any one of claims 1 to 6.

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