Simulation optimization method and device for noise reduction performance of film type acoustic metamaterial

Abstract

The invention provides a method and a device for simulating and optimizing noise reduction performance of a thin film type acoustic metamaterial, relates to the technical field of acoustic metamaterials, and can be applied to a scene in which the noise reduction performance of the thin film type acoustic metamaterial is simulated and optimized. According to the specific implementation scheme, the method comprises the steps that simulation modeling is conducted on an acoustic metamaterial structure, an acoustic metamaterial simulation model is obtained, and the acoustic metamaterial simulation model comprises at least one acoustic metamaterial structure; determining the noise reduction performance of the acoustic metamaterial simulation model through an acoustic simulation algorithm; parameters of the acoustic metamaterial simulation model are optimized according to a preset noise reduction performance target and the attribute features corresponding to the preset scene, an optimized acoustic metamaterial simulation model meeting the preset noise reduction performance target is obtained, and the optimized acoustic metamaterial simulation model comprises at least one optimized acoustic metamaterial structure. The vibration and noise reduction efficiency of the film type acoustic metamaterial can be improved, and the operand is reduced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of acoustic metamaterials, and in particular to a method and device for simulating and optimizing the noise reduction performance of thin-film acoustic metamaterials, which can be applied to the scenario of simulating and optimizing the noise reduction performance of thin-film acoustic metamaterials. Background Art

[0002] Thin-film acoustic metamaterial is a new type of material that can be widely used in vibration and noise reduction in industrial production, transportation, construction, aerospace and other fields.

[0003] Currently, thin-film acoustic metamaterials are made by attaching rigid oscillators to flexible thin-film materials to form a spring-oscillator system. The material parameters are set through experience and intuition, and then the sound insulation performance of the acoustic metamaterial is calculated using finite element software to achieve the expected vibration and noise reduction capabilities.

[0004] However, the current method of reducing vibration and noise through thin-film acoustic metamaterials is inefficient and requires a lot of computation. Summary of the invention

[0005] The present invention provides a method and device for simulating and optimizing the noise reduction performance of a thin-film acoustic metamaterial, which can improve the vibration reduction and noise reduction efficiency of the thin-film acoustic metamaterial and reduce the amount of calculation.

[0006] According to a first aspect of the present disclosure, a method for simulating and optimizing the noise reduction performance of a thin-film acoustic metamaterial is provided, the method comprising: simulating and modeling an acoustic metamaterial structure to obtain an acoustic metamaterial simulation model, the acoustic metamaterial simulation model comprising at least one acoustic metamaterial structure; determining the noise reduction performance of the acoustic metamaterial simulation model through an acoustic simulation algorithm; optimizing parameters of the acoustic metamaterial simulation model according to a preset noise reduction performance target and attribute characteristics corresponding to a preset scenario, to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target, the optimized acoustic metamaterial simulation model comprising at least one optimized acoustic metamaterial structure.

[0007] In some possible implementations, the attribute features corresponding to the preset scene include a spatial structure corresponding to the preset scene and a sound source parameter corresponding to the preset scene.

[0008] In some possible implementations, when the acoustic metamaterial simulation model includes at least two acoustic metamaterial structures, the acoustic metamaterial structure is simulated and modeled to obtain the acoustic metamaterial simulation model, including: simulating and modeling the acoustic metamaterial structure according to a preset arrangement to obtain the acoustic metamaterial simulation model; the optimized acoustic metamaterial simulation model includes at least two optimized acoustic metamaterial structures.

[0009] In some possible implementations, the parameters of the acoustic metamaterial simulation model are optimized according to the preset noise reduction performance target and the property characteristics corresponding to the preset scenario to obtain the optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target, including: according to the preset noise reduction performance target and the property characteristics corresponding to the preset scenario, the parameters of the acoustic metamaterial simulation model are optimized by a preset algorithm to obtain the optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target.

[0010] In some possible implementations, the preset algorithm includes a genetic algorithm, and the parameters of the acoustic metamaterial simulation model are optimized by the preset algorithm according to the preset noise reduction performance target and the attribute characteristics corresponding to the preset scenario to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target, including: initializing the parameters of the acoustic metamaterial simulation model to obtain an initial population of the genetic algorithm; calculating the acoustic transmission loss of the acoustic metamaterial simulation model corresponding to the genetic individual, and constructing an objective function using the acoustic transmission loss according to the preset noise reduction performance target; optimizing the parameters of the acoustic metamaterial simulation model according to the objective function until an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target is obtained.

[0011] In some possible implementations, the preset noise reduction performance target includes at least one of the following: noise reduction amount, noise reduction frequency range, and stability.

[0012] In some possible implementations, the bandgap width and position of the optimized acoustic metamaterial simulation model are calculated according to the parameters of the optimized acoustic metamaterial simulation model, and the bandgap width and position are used to verify the noise reduction performance of the optimized acoustic metamaterial simulation model.

[0013] The first aspect of the present disclosure has at least the following beneficial effects: by simulating and modeling the acoustic metamaterial structure, an acoustic metamaterial simulation model is obtained, and then the noise reduction performance of the acoustic metamaterial simulation model is determined by an acoustic simulation algorithm; finally, the parameters of the acoustic metamaterial simulation model are optimized according to the preset noise reduction performance target and the attribute characteristics corresponding to the preset scene, and the optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target is obtained. The efficiency of vibration reduction and noise reduction of thin-film acoustic metamaterials can be improved, the amount of calculation for determining the vibration reduction and noise reduction performance of thin-film acoustic metamaterials can be reduced, and the applicability of thin-film acoustic metamaterials in different application scenarios can be improved.

[0014] According to a second aspect of the present disclosure, a device for simulating and optimizing the noise reduction performance of a thin-film acoustic metamaterial is provided, the device comprising:

[0015] A modeling unit, configured to perform simulation modeling on the acoustic metamaterial structure to obtain an acoustic metamaterial simulation model, wherein the acoustic metamaterial simulation model includes at least one acoustic metamaterial structure;

[0016] A determination unit, used to determine the noise reduction performance of the acoustic metamaterial simulation model through an acoustic simulation algorithm;

[0017] The optimization unit is used to optimize the parameters of the acoustic metamaterial simulation model according to the preset noise reduction performance target and the property characteristics corresponding to the preset scene, so as to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target, and the optimized acoustic metamaterial simulation model includes at least one optimized acoustic metamaterial structure.

[0018] Optionally, the attribute features corresponding to the preset scene include a spatial structure corresponding to the preset scene and a sound source parameter corresponding to the preset scene.

[0019] Optionally, when the acoustic metamaterial simulation model includes at least two acoustic metamaterial structures, the modeling unit is specifically used to: simulate and model the acoustic metamaterial structure according to a preset arrangement to obtain the acoustic metamaterial simulation model; the optimized acoustic metamaterial simulation model includes at least two optimized acoustic metamaterial structures.

[0020] Optionally, the optimization unit is specifically used to optimize the parameters of the acoustic metamaterial simulation model through a preset algorithm according to the preset noise reduction performance target and the property characteristics corresponding to the preset scene, so as to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target.

[0021] Optionally, the preset algorithm includes a genetic algorithm and an optimization unit, which is specifically used to: initialize the parameters of the acoustic metamaterial simulation model to obtain an initial population of the genetic algorithm; calculate the sound transmission loss of the acoustic metamaterial simulation model corresponding to the genetic individual, and construct an objective function using the sound transmission loss according to a preset noise reduction performance target; optimize the parameters of the acoustic metamaterial simulation model according to the objective function until an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target is obtained.

[0022] Optionally, the preset noise reduction performance target includes at least one of the following: noise reduction amount, noise reduction frequency range, and stability.

[0023] Optionally, the device also includes: a calculation unit, used to calculate the band gap width and position of the optimized acoustic metamaterial simulation model according to the parameters of the optimized acoustic metamaterial simulation model, and the band gap width and position are used to verify the noise reduction performance of the optimized acoustic metamaterial simulation model.

[0024] According to a third aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the first aspect.

[0025] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the method according to the first aspect.

[0026] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method according to the first aspect.

[0027] The beneficial effects of the second to fifth aspects of the present disclosure can refer to the beneficial effects of the first aspect and will not be repeated here.

[0028] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0030] Figure 1 A schematic diagram of a flow chart of a method for simulating and optimizing the noise reduction performance of a thin-film acoustic metamaterial provided in an embodiment of the present disclosure;

[0031] Figure 2 A schematic diagram of an acoustic metamaterial simulation model provided in an embodiment of the present disclosure;

[0032] Figure 3 Another schematic diagram of the acoustic metamaterial simulation model provided by the embodiment of the present disclosure;

[0033] Figure 4 A schematic diagram of another acoustic metamaterial simulation model provided in an embodiment of the present disclosure;

[0034] Figure 5 Another schematic diagram of the process of simulating and optimizing the noise reduction performance of a thin-film acoustic metamaterial provided in an embodiment of the present disclosure;

[0035] Figure 6 A schematic diagram of the composition of a device for simulating and optimizing the noise reduction performance of a thin-film acoustic metamaterial provided in an embodiment of the present disclosure;

[0036] Figure 7 FIG. 7 is a schematic block diagram of an example electronic device 700 that may be used to implement embodiments of the present disclosure. DETAILED DESCRIPTION

[0037] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0038] It should be understood that in the embodiments of the present disclosure, the character " / " generally indicates that the objects associated with each other are in an "or" relationship. The terms "first", "second", etc. are used only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features.

[0039] Thin-film acoustic metamaterial is a new type of material that can be widely used in vibration and noise reduction in industrial production, transportation, construction, aerospace and other fields.

[0040] Currently, thin-film acoustic metamaterials are made by attaching rigid oscillators to flexible thin-film materials to form a spring-oscillator system. The material parameters are set through experience and intuition, and then the sound insulation performance of the acoustic metamaterial is calculated using finite element software to achieve the expected vibration and noise reduction capabilities.

[0041] However, the current method of reducing vibration and noise through thin-film acoustic metamaterials is inefficient and requires a lot of computation.

[0042] For example, a thin film acoustic metamaterial is a new type of material, the key of which is to attach a rigid oscillator to a flexible thin film material. This structure can be regarded as a spring-oscillator system, in which the flexible thin film material provides spring-like elasticity, while the rigid oscillator simulates the mass of the oscillator. Thin film acoustic metamaterials have broad application prospects in many fields, including industrial production, transportation, construction, and aerospace. In these fields, vibration reduction and noise reduction are an important demand because vibration and noise may have adverse effects on equipment, structures or the environment. At present, the design of thin film acoustic metamaterials mainly relies on experience and intuition to set material parameters. This means that designers may predict which parameters may produce the desired vibration reduction and noise reduction effects based on past experience or intuition. After setting the parameters, finite element software is used to calculate the sound insulation performance of the acoustic metamaterial. By simulating the response of the material under different frequencies and conditions, the software can predict its ability to reduce vibration and noise. Although current methods can design thin film acoustic metamaterials with certain vibration reduction and noise reduction capabilities, their efficiency is low. This may be because the design method based on experience and intuition is not accurate enough and requires multiple iterations and adjustments to achieve the desired effect. Another problem is the large amount of calculations. Since a large number of simulation calculations need to be performed using finite element software, this may consume a lot of computing resources and time. This not only increases the cost of the design, but may also limit the speed and flexibility of the design.

[0043] Against this background, the present disclosure provides a method for simulating and optimizing the noise reduction performance of thin-film acoustic metamaterials, which can improve the vibration reduction and noise reduction efficiency of thin-film acoustic metamaterials and reduce the amount of computation.

[0044] Exemplarily, the method for simulating and optimizing the noise reduction performance of thin-film acoustic metamaterials provided in the present disclosure can be applied to the scenario of simulating and optimizing the noise reduction performance of thin-film acoustic metamaterials.

[0045] For example, the execution subject of the method for simulating and optimizing the noise reduction performance of thin-film acoustic metamaterials provided in the embodiments of the present disclosure may be a computer or a server, or may be other devices with data processing capabilities. The execution subject of the method is not limited here.

[0046] In some embodiments, the server may be a single server, or a server cluster composed of multiple servers. In some implementations, the server cluster may also be a distributed cluster. The present disclosure does not limit the specific implementation of the server.

[0047] Figure 1 The figure is a flow chart of a method for simulating and optimizing the noise reduction performance of a thin-film acoustic metamaterial provided in an embodiment of the present disclosure. Figure 1 As shown, the method may include S101-S103.

[0048] S101, performing simulation modeling on the acoustic metamaterial structure to obtain an acoustic metamaterial simulation model.

[0049] The acoustic metamaterial simulation model includes at least one acoustic metamaterial structure.

[0050] For example, the acoustic metamaterial structure may be a structure consisting of a frame, a film, and a mass block. Figure 2 Schematic diagram of an acoustic metamaterial simulation model provided by an embodiment of the present disclosure. Figure 2 As shown, the acoustic metamaterial simulation model includes an acoustic metamaterial structure, wherein 1 represents a frame, 2 represents a film, and 3 represents a mass block. The acoustic metamaterial structure can be modeled using professional modeling software to obtain a three-dimensional simulation model of the acoustic metamaterial structure, namely, an acoustic metamaterial simulation model.

[0051] S102: Determine the noise reduction performance of the acoustic metamaterial simulation model through an acoustic simulation algorithm.

[0052] For example, after obtaining the acoustic metamaterial simulation model, the model can be solved by an acoustic simulation algorithm (e.g., finite element method, boundary element method, finite difference time domain method, etc.) to obtain information such as the propagation, scattering and interaction of sound waves in the model. Based on this information, acoustic performance parameters such as the transmission coefficient, reflection coefficient and sound absorption coefficient of the acoustic metamaterial simulation model at different frequencies can be calculated. Based on the acoustic performance parameters obtained by simulation calculation, the noise reduction performance of the acoustic metamaterial simulation model can be evaluated.

[0053] S103, optimizing the parameters of the acoustic metamaterial simulation model according to the preset noise reduction performance target and the property characteristics corresponding to the preset scene, to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target.

[0054] The optimized acoustic metamaterial simulation model includes at least one optimized acoustic metamaterial structure.

[0055] Exemplarily, the preset scenes may include buildings, vehicles, industrial equipment, etc. The attribute features corresponding to the preset scenes may include the spatial structure, size, internal materials, and sound source parameters corresponding to the preset scenes. The spatial structure corresponding to the preset scenes may include the spatial structure of different types of buildings (e.g., the structure of floors, rooms, walls, support columns, floors, ceilings, etc.), the spatial structure of different types of vehicles (e.g., the structure of different types of cars, trains, airplanes, etc.), and the spatial structure of different types of industrial equipment (e.g., the structure of generator sets, compressors, fans, etc.). The sound source parameters corresponding to the preset scenes may include the location of the sound source, the frequency of the sound source, the intensity of the sound source, etc. The preset noise reduction performance targets may include the amount of noise reduction, the frequency range of noise reduction, stability, etc. Different scenes have different structures, different materials, and different requirements for sound insulation and noise reduction, and the structures of applicable acoustic metamaterials are also different. The parameters of the acoustic metamaterial simulation model can be optimized according to the preset noise reduction performance target and the attribute characteristics corresponding to different scenarios to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target, wherein the parameters of the acoustic metamaterial simulation model may include model length, width, frame thickness, width, film length, width, thickness, mass block radius, height, weight, etc.

[0056] For example, when the preset noise reduction performance target is the noise reduction frequency range: 280 Hz ~ 380 Hz, and the preset scene is all the walls of the room, the parameters of the acoustic metamaterial simulation model can be optimized according to the preset noise reduction frequency range of 280 Hz ~ 380 Hz, the material and area of ​​all the walls of the room, and the demand for sound insulation and noise reduction to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target, and then the acoustic metamaterial structure suitable for all the walls of the room can be determined according to the optimized acoustic metamaterial simulation model.

[0057] The disclosed embodiment simulates and models the acoustic metamaterial structure to obtain an acoustic metamaterial simulation model, and then determines the noise reduction performance of the acoustic metamaterial simulation model through an acoustic simulation algorithm; finally, the parameters of the acoustic metamaterial simulation model are optimized according to the preset noise reduction performance target and the attribute characteristics corresponding to the preset scene, and an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target is obtained. The efficiency of vibration reduction and noise reduction of thin-film acoustic metamaterials can be improved, the amount of calculation for determining the vibration reduction and noise reduction performance of thin-film acoustic metamaterials can be reduced, and the applicability of thin-film acoustic metamaterials in different application scenarios can be improved.

[0058] In some embodiments, when the acoustic metamaterial simulation model includes at least two acoustic metamaterial structures, the acoustic metamaterial structure is simulated and modeled to obtain the acoustic metamaterial simulation model, including: simulating and modeling the acoustic metamaterial structure according to a preset arrangement to obtain the acoustic metamaterial simulation model; the optimized acoustic metamaterial simulation model includes at least two optimized acoustic metamaterial structures.

[0059] Exemplarily, when the acoustic metamaterial simulation model includes at least two acoustic metamaterial structures, the arrangement of at least two acoustic metamaterial structures can be preset, and the at least two acoustic metamaterial structures are simulated and modeled according to the preset arrangement to obtain an acoustic metamaterial simulation model of a multi-cell structure; then, the parameters of the acoustic metamaterial simulation model are optimized according to the preset noise reduction performance target and the attribute characteristics corresponding to the preset scene to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target, and the optimized acoustic metamaterial simulation model includes at least two optimized acoustic metamaterial structures. Among them, the preset arrangement method may include a periodic arrangement or a non-periodic arrangement. The periodic arrangement means that the acoustic metamaterial structure is arranged according to a predetermined periodic pattern, such as a matrix arrangement in a two-dimensional plane or a lattice arrangement in a three-dimensional space. The non-periodic arrangement means that the acoustic metamaterial structure is arranged according to a non-predetermined, random or specific non-periodic pattern, such as a disordered arrangement or a local optimized arrangement according to specific sound field requirements.

[0060] For example, Figure 3 Schematic diagram of another acoustic metamaterial simulation model provided by the embodiment of the present disclosure. Figure 3 As shown, the acoustic metamaterial simulation model includes 8 acoustic metamaterial structures, and the 8 acoustic metamaterial structures are randomly arranged, namely Figure 3 The arrangement is non-periodic. Figure 4 Schematic diagram of another acoustic metamaterial simulation model provided by the embodiment of the present disclosure. Figure 4 As shown, the acoustic metamaterial simulation model includes 6 acoustic metamaterial structures, and the 6 acoustic metamaterial structures are arranged in a 2×3 matrix arrangement, namely Figure 4 It can be arranged periodically. It can be arranged according to the preset noise reduction performance target and the attribute characteristics corresponding to the preset scene. Figure 3 or Figure 4 The parameters of the polyhedral acoustic metamaterial simulation model shown are optimized to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target.

[0061] In this embodiment, when the acoustic metamaterial simulation model includes at least two acoustic metamaterial structures, the acoustic metamaterial structure is simulated and modeled according to a preset arrangement to obtain the acoustic metamaterial simulation model; the unit cell structure of the acoustic metamaterial structure can be arranged according to different arrangements to obtain polyhedral structures with different arrangements, and then the parameters of the arranged polyhedral structures are optimized according to the preset noise reduction performance target and the property characteristics corresponding to the preset scene, thereby reducing the amount of calculation for parameter optimization of the acoustic metamaterial simulation model and improving the applicability of the acoustic metamaterial in different application scenarios.

[0062] In some embodiments, the parameters of the acoustic metamaterial simulation model are optimized according to the preset noise reduction performance target and the property characteristics corresponding to the preset scenario to obtain the optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target, including: according to the preset noise reduction performance target and the property characteristics corresponding to the preset scenario, the parameters of the acoustic metamaterial simulation model are optimized by a genetic algorithm to obtain the optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target.

[0063] For example, the genetic algorithm can solve complex optimization problems. It provides an effective search and optimization strategy by simulating natural selection and genetic principles, and can automatically find and optimize the solution to the problem in the search space. According to the preset noise reduction performance target and the attribute characteristics corresponding to the preset scene, the parameters of the acoustic metamaterial simulation model can be optimized by the genetic algorithm to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target.

[0064] Optionally, the parameters of the acoustic metamaterial simulation model may also be optimized by a particle swarm optimization algorithm, a gradient descent method, or the like.

[0065] This embodiment optimizes the parameters of the acoustic metamaterial simulation model through a preset algorithm according to the preset noise reduction performance target and the attribute characteristics corresponding to the preset scenario, and obtains an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target. Through strict calculation and iteration of the algorithm, a parameter combination that is more in line with physical laws can be found, thereby enhancing the reliability of the acoustic metamaterial simulation model.

[0066] Figure 5 Another schematic diagram of the process of simulating and optimizing the noise reduction performance of a thin-film acoustic metamaterial provided in an embodiment of the present disclosure. Figure 5 As shown, the method may include S501-S503.

[0067] S501 , initializing the parameters of the acoustic metamaterial simulation model to obtain an initial population of the genetic algorithm.

[0068] Exemplarily, when the preset algorithm is a genetic algorithm, a group of parameters of the acoustic metamaterial simulation model can be randomly generated as an initial population of the genetic algorithm according to the parameter range of the acoustic metamaterial simulation model obtained by modeling.

[0069] S502: Calculate the acoustic transmission loss of the acoustic metamaterial simulation model corresponding to the genetic individual, and construct an objective function using the acoustic transmission loss according to a preset noise reduction performance target.

[0070] For example, the acoustic transmission loss of the acoustic metamaterial simulation model can be calculated by the finite element method according to the parameters obtained by initialization, and then the objective function can be constructed by using the acoustic transmission loss according to the preset noise reduction performance target. The objective function can be set to maximize the noise reduction within a certain frequency band.

[0071] S503 , optimizing the parameters of the acoustic metamaterial simulation model according to the objective function until an optimized acoustic metamaterial simulation model that meets a preset noise reduction performance target is obtained.

[0072] Exemplarily, after constructing the objective function, the objective function value can be used as a measure of the fitness of the genetic individual, and then selection, crossover and mutation genetic operations are performed in sequence to generate the next generation of population, so that the population can continuously evolve and update, and finally the parameters of the acoustic metamaterial simulation model that meets the preset noise reduction performance target are obtained, that is, the optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target is obtained.

[0073] This embodiment initializes the parameters of the acoustic metamaterial simulation model to obtain the initial population of the genetic algorithm, then calculates the acoustic transmission loss of the acoustic metamaterial simulation model corresponding to the genetic individual, and constructs an objective function based on the preset noise reduction performance target using the acoustic transmission loss. Finally, the parameters of the acoustic metamaterial simulation model are optimized based on the objective function, and an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target can be obtained. This can adapt to different optimization targets or scenario requirements, and improves the flexibility of optimizing the parameters of the acoustic metamaterial simulation model.

[0074] In some embodiments, the above method may further include: calculating the band gap width and position of the optimized acoustic metamaterial simulation model according to the parameters of the optimized acoustic metamaterial simulation model, and the band gap width and position are used to verify the noise reduction performance of the optimized acoustic metamaterial simulation model.

[0075] Exemplarily, a bandgap refers to the frequency interval in which an acoustic metamaterial can significantly suppress the propagation of sound waves. Its size and position have an important influence on the noise reduction performance of a thin-film acoustic metamaterial. The width of the bandgap directly affects the noise reduction performance. A wider bandgap means that the acoustic metamaterial can effectively suppress sound waves in a wider frequency range. This is suitable for application scenarios that need to deal with noise of multiple frequency components, such as urban traffic noise, industrial equipment noise, etc. By designing a thin-film acoustic metamaterial with a wider bandgap frequency range, effective noise reduction can be achieved for these complex noise sources. The position of the bandgap also has an important influence on the noise reduction performance. If the bandgap covers the main frequency components that need to be noise-reduced, the acoustic metamaterial can strongly suppress the sound waves of these frequencies, thereby improving the noise reduction performance. In addition, the stability and adjustability of the bandgap are also key factors affecting the noise reduction performance. Some acoustic metamaterials have an adjustable bandgap frequency range, and the position and width of the bandgap can be changed by external stimuli (such as temperature, pressure, electric field, etc.). This adjustability enables acoustic metamaterials to adapt to noise sources of different frequencies and improve the flexibility and adaptability of noise reduction. The noise reduction performance of the optimized acoustic metamaterial simulation model can also be verified by calculating the band gap width and position of the optimized acoustic metamaterial simulation model.

[0076] This embodiment calculates the band gap width and position of the optimized acoustic metamaterial simulation model according to the parameters of the optimized acoustic metamaterial simulation model, and can verify the noise reduction performance of the optimized acoustic metamaterial simulation model, thereby improving the accuracy of the optimization parameters of the acoustic metamaterial simulation model.

[0077] In an exemplary embodiment, the present disclosure also provides a device for simulating and optimizing the noise reduction performance of a thin film acoustic metamaterial, which can be used to implement the method for simulating and optimizing the noise reduction performance of a thin film acoustic metamaterial as described in the aforementioned embodiment. Figure 6 This is a schematic diagram of the composition of the thin film acoustic metamaterial noise reduction performance simulation and optimization device provided in the embodiment of the present disclosure. Figure 5 As shown, the device may include: a modeling unit 601, a determining unit 602, and an optimizing unit 603.

[0078] A modeling unit 601 is used to perform simulation modeling on an acoustic metamaterial structure to obtain an acoustic metamaterial simulation model, wherein the acoustic metamaterial simulation model includes at least one acoustic metamaterial structure;

[0079] A determination unit 602 is used to determine the noise reduction performance of the acoustic metamaterial simulation model through an acoustic simulation algorithm;

[0080] The optimization unit 603 is used to optimize the parameters of the acoustic metamaterial simulation model according to the preset noise reduction performance target and the property characteristics corresponding to the preset scene, so as to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target, and the optimized acoustic metamaterial simulation model includes at least one optimized acoustic metamaterial structure.

[0081] Optionally, the attribute features corresponding to the preset scene include a spatial structure corresponding to the preset scene and a sound source parameter corresponding to the preset scene.

[0082] Optionally, the modeling unit 601 is specifically used to: perform simulation modeling on the acoustic metamaterial structure according to a preset arrangement to obtain an acoustic metamaterial simulation model; the optimized acoustic metamaterial simulation model includes at least two optimized acoustic metamaterial structures.

[0083] Optionally, the optimization unit 603 is specifically used to optimize the parameters of the acoustic metamaterial simulation model through a preset algorithm according to the preset noise reduction performance target and the property characteristics corresponding to the preset scene, so as to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target.

[0084] Optionally, the preset algorithm may include a genetic algorithm, and the optimization unit 603 is specifically used to: initialize the parameters of the acoustic metamaterial simulation model to obtain an initial population of the genetic algorithm; calculate the sound transmission loss of the acoustic metamaterial simulation model corresponding to the genetic individual, and construct an objective function using the sound transmission loss according to a preset noise reduction performance target; optimize the parameters of the acoustic metamaterial simulation model according to the objective function until an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target is obtained.

[0085] Optionally, the preset noise reduction performance target includes at least one of the following: noise reduction amount, noise reduction frequency range, and stability.

[0086] Alternatively, if Figure 6 As shown, the above-mentioned device also includes: a calculation unit 604, which is used to calculate the band gap width and position of the optimized acoustic metamaterial simulation model according to the parameters of the optimized acoustic metamaterial simulation model, and the band gap width and position are used to verify the noise reduction performance of the optimized acoustic metamaterial simulation model.

[0087] The beneficial effects of the above-mentioned thin-film acoustic metamaterial noise reduction performance simulation and optimization device can refer to the beneficial effects of the thin-film acoustic metamaterial noise reduction performance simulation and optimization method described in the above-mentioned embodiment, which will not be repeated here.

[0088] According to an embodiment of the present disclosure, the present disclosure further provides an electronic device. The electronic device may be a server, computer or other device as described in the above embodiments, and can be used to implement the method for simulating and optimizing the noise reduction performance of thin-film acoustic metamaterials provided in the embodiments of the present disclosure.

[0089] In an exemplary embodiment, the electronic device may include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the method for simulating and optimizing the noise reduction performance of thin film acoustic metamaterials as described in the above embodiments.

[0090] For example, Figure 7 Schematic block diagram of an example electronic device 700 that can be used to implement an embodiment of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0091] like Figure 7 As shown, the electronic device 700 may include a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit 708 into a random access memory (RAM). In RAM 703, various programs and data required for the operation of the electronic device 700 can also be stored. The computing unit 701, ROM 702, and RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface is also connected to the bus 704.

[0092] Multiple components in the electronic device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the electronic device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0093] The computing unit 701 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above, such as a thin film acoustic metamaterial noise reduction performance simulation optimization method. For example, in some embodiments, the thin film acoustic metamaterial noise reduction performance simulation optimization method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 708.

[0094] In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the above-described method for simulating and optimizing the noise reduction performance of the thin film acoustic metamaterial may be performed.

[0095] Alternatively, in other embodiments, the computing unit 701 may be configured to execute the thin film acoustic metamaterial noise reduction performance simulation optimization method in any other appropriate manner (for example, by means of firmware).

[0096] According to an embodiment of the present disclosure, the present disclosure also provides a readable storage medium and a computer program product.

[0097] In an exemplary embodiment, the readable storage medium may be a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method according to the above embodiments.

[0098] In an exemplary embodiment, a computer program product includes a computer program, and when the computer program is executed by a processor, the method according to the above embodiments is implemented.

[0099] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0100] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0101] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0102] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0103] The systems and techniques described herein can be implemented in computing systems that include back-end components (e.g., as data servers), computing systems that include middleware components (e.g., application servers), computing systems that include front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), and computing systems that include any combination of such back-end components, middleware components, and front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area networks (LANs), wide area networks (WANs), and the Internet.

[0104] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0105] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0106] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for simulating and optimizing the noise reduction performance of a thin-film acoustic metamaterial, the method comprising: Performing simulation modeling on the acoustic metamaterial structure to obtain an acoustic metamaterial simulation model, wherein the acoustic metamaterial simulation model includes at least one of the acoustic metamaterial structures; Determining the noise reduction performance of the acoustic metamaterial simulation model through an acoustic simulation algorithm; Optimizing the parameters of the acoustic metamaterial simulation model according to the preset noise reduction performance target and the property characteristics corresponding to the preset scene, to obtain the optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target, wherein the optimized acoustic metamaterial simulation model includes at least one optimized acoustic metamaterial structure; The preset scene includes at least one of the following: buildings, vehicles, and industrial equipment; The attribute features corresponding to the preset scene include a spatial structure corresponding to the preset scene and a sound source parameter corresponding to the preset scene.

2. According to the method of claim 1, when the acoustic metamaterial simulation model includes at least two acoustic metamaterial structures, the step of simulating and modeling the acoustic metamaterial structure to obtain the acoustic metamaterial simulation model comprises: Performing simulation modeling on the acoustic metamaterial structure according to a preset arrangement to obtain a simulation model of the acoustic metamaterial; The optimized acoustic metamaterial simulation model includes at least two optimized acoustic metamaterial structures. 3 . The method according to claim 2 , wherein the preset arrangement comprises a periodic arrangement or a non-periodic arrangement.

4. The method according to claim 2, wherein the parameters of the acoustic metamaterial simulation model are optimized according to the preset noise reduction performance target and the property characteristics corresponding to the preset scene to obtain the optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target, comprising: According to the preset noise reduction performance target and the property characteristics corresponding to the preset scene, the parameters of the acoustic metamaterial simulation model are optimized by a genetic algorithm to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target; The method of optimizing the parameters of the acoustic metamaterial simulation model by a genetic algorithm according to the preset noise reduction performance target and the property characteristics corresponding to the preset scene to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target includes: Initializing the parameters of the acoustic metamaterial simulation model to obtain the initial population of the genetic algorithm; Calculating the acoustic transmission loss of the acoustic metamaterial simulation model corresponding to the genetic individual, and constructing an objective function using the acoustic transmission loss according to the preset noise reduction performance target; The parameters of the acoustic metamaterial simulation model are optimized according to the objective function until the optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target is obtained.

5. According to the method according to any one of claims 1 to 4, the preset noise reduction performance target includes at least one of the following: noise reduction amount, noise reduction frequency range, and stability.

6. The method according to claim 2, further comprising: According to the parameters of the optimized acoustic metamaterial simulation model, the band gap width and position of the optimized acoustic metamaterial simulation model are calculated, and the band gap width and position are used to verify the noise reduction performance of the optimized acoustic metamaterial simulation model.

7. A device for simulating and optimizing the noise reduction performance of a thin-film acoustic metamaterial, the device comprising: A modeling unit, configured to perform simulation modeling on the acoustic metamaterial structure to obtain an acoustic metamaterial simulation model, wherein the acoustic metamaterial simulation model includes at least one of the acoustic metamaterial structures; A determination unit, configured to determine the noise reduction performance of the acoustic metamaterial simulation model through an acoustic simulation algorithm; An optimization unit, configured to optimize the parameters of the acoustic metamaterial simulation model according to a preset noise reduction performance target and property characteristics corresponding to a preset scenario, to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target, wherein the optimized acoustic metamaterial simulation model includes at least one optimized acoustic metamaterial structure; The attribute features corresponding to the preset scene include the spatial structure corresponding to the preset scene and the sound source parameters corresponding to the preset scene; When the acoustic metamaterial simulation model includes at least two acoustic metamaterial structures, the modeling unit is specifically used to: Performing simulation modeling on the acoustic metamaterial structure according to a preset arrangement to obtain a simulation model of the acoustic metamaterial; The optimized acoustic metamaterial simulation model includes at least two optimized acoustic metamaterial structures.

8. The device according to claim 7, wherein the optimization unit is specifically configured to: According to the preset noise reduction performance target and the property characteristics corresponding to the preset scene, the parameters of the acoustic metamaterial simulation model are optimized by a preset algorithm to obtain an optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target.

9. The device according to claim 8, wherein the preset algorithm comprises a genetic algorithm, and the optimization unit is specifically configured to: Initializing the parameters of the acoustic metamaterial simulation model to obtain the initial population of the genetic algorithm; Calculating the acoustic transmission loss of the acoustic metamaterial simulation model corresponding to the genetic individual, and constructing an objective function using the acoustic transmission loss according to the preset noise reduction performance target; The parameters of the acoustic metamaterial simulation model are optimized according to the objective function until the optimized acoustic metamaterial simulation model that meets the preset noise reduction performance target is obtained.

10. The device according to claim 8, further comprising: A calculation unit is used to calculate the band gap width and position of the optimized acoustic metamaterial simulation model according to the parameters of the optimized acoustic metamaterial simulation model, wherein the band gap width and position are used to verify the noise reduction performance of the optimized acoustic metamaterial simulation model.

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