A method for optimizing design of a multi-cavity parallel cooperative sound absorption metamaterial structure
By combining finite element analysis with particle swarm optimization algorithm, a multi-cavity parallel synergistic sound-absorbing metamaterial structure was designed, which solved the problems of narrow sound absorption bandwidth and difficulty in low-frequency control in traditional design, and achieved broadband and efficient sound absorption effect.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HEFEI UNIV OF TECH
- Filing Date
- 2026-05-13
- Publication Date
- 2026-07-31
AI Technical Summary
Traditional micro-perforated plate structures have narrow sound absorption bandwidth and are difficult to control at low frequencies. Existing multi-cavity parallel structure designs rely on experience and are difficult to obtain the optimal parameters accurately.
By combining finite element analysis with particle swarm optimization, a multi-cavity parallel synergistic sound-absorbing metamaterial structure is constructed. The array arrangement of non-uniform Helmholtz resonant units and the iterative optimization of the particle swarm optimization algorithm are used to optimize the design structural parameters to improve sound absorption performance.
This study achieves broadband and efficient sound absorption performance of a multi-cavity parallel synergistic sound-absorbing metamaterial structure, improving sound absorption performance and design quality within the target frequency band, and solving the problem of inaccurate parameters in traditional designs.
Smart Images
Figure CN122490920A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of acoustic metamaterial structure design and optimization technology, specifically involving a multi-cavity parallel synergistic sound-absorbing metamaterial structure optimization design method based on the combination of finite element analysis and parameter optimization. Background Technology
[0002] Sound absorption and noise reduction technology is widely used in green buildings, transportation vehicles, and other fields. However, traditional micro-perforated panel structures suffer from problems such as narrow sound absorption bandwidth and difficulty in low-frequency control, making it difficult to meet the requirements for high-efficiency sound absorption.
[0003] Acoustic metamaterials are materials composed of specially designed subwavelength periodic structures, possessing extraordinary physical properties not found in natural materials, enabling the manipulation of sound waves. In recent years, the Helmholtz resonator (HRU), as a classic acoustic control device, has demonstrated strong innovative potential in the field of acoustic metamaterials. To broaden the sound absorption frequency range and improve sound absorption performance within the target frequency band, multiple different HRUs are combined in parallel to form a multi-cavity synergistic sound absorption structure.
[0004] However, the design of the above-mentioned parallel structures currently relies heavily on experience-based design or manual parameter tuning, and it is difficult to accurately obtain the optimal structural design parameters using these methods. Summary of the Invention
[0005] To address the problems of narrow sound absorption bandwidth and difficulty in low-frequency control of traditional Helmholtz resonators, this invention provides an optimized design method for a multi-cavity parallel synergistic sound-absorbing metamaterial structure, aiming to achieve optimized design of the multi-cavity parallel synergistic sound-absorbing metamaterial structure and effectively improve the average sound absorption coefficient in the target frequency band.
[0006] The present invention provides an optimized design method for a multi-cavity parallel synergistic sound-absorbing metamaterial structure, characterized by the following steps: Step 1: Construct an initial structural model of a multi-cavity parallel synergistic sound-absorbing metamaterial. The initial structural model consists of n×n non-uniform Helmholtz resonant units (HRUs) arranged in a preset array. Each HRU consists of a cavity and an extended neck connected to it. The structural parameters of different HRUs are different, thus forming a non-uniform structure. Step 2: Establish the finite element simulation model corresponding to the initial structural model, and use the structural parameters of HRU as structural design variables, including: cavity length, extended neck length and extended neck radius. Analyze the sound absorption performance of the finite element simulation model under different structural design variables to obtain the corresponding sound absorption coefficient curve and sound absorption results in the target frequency band. Step 3: Input the structural design variables corresponding to the current position of the particle into the finite element simulation model to calculate the sound absorption coefficient in the target frequency band. With the maximum average sound absorption coefficient in the target frequency band as the optimization objective, the particle swarm optimization algorithm is used to iteratively optimize the structural design variables of each particle. The optimization results are then transmitted to the finite element simulation model for simulation until the preset termination condition is met, and the optimal structural design variables are obtained. Step 4: Based on the optimal structural design variables, obtain the design scheme of the multi-cavity parallel synergistic sound-absorbing metamaterial structure.
[0007] The optimization design method for a multi-cavity parallel synergistic sound-absorbing metamaterial structure described in this invention is also characterized in that step 2 includes the following steps: Step 2.1: Establish a finite element simulation model of the multi-cavity parallel synergistic sound-absorbing metamaterial structure in COMSOL software; Step 2.2: Set the structural design variables for the finite element simulation model. The range of the structural design variables is determined by structural size limitations, manufacturing constraints, and target frequency band requirements. Step 2.3: Set the plane wave to be incident along the direction directly opposite the HRU, and after setting a perfectly matched layer on the top surface of the HRU, mesh the finite element simulation model, and then calculate the sound absorption performance of the meshed finite element simulation model in the target frequency band to obtain the corresponding sound absorption coefficient curve and the sound absorption results in the target frequency band.
[0008] Furthermore, in step 3, the average sound absorption coefficient within the target frequency band is calculated using equation (1). : (1) In equation (1), This represents the total number of discrete points within the target frequency band. This represents the frequency of the k-th discrete point. yes The sound absorption coefficient at that location.
[0009] The present invention provides an electronic device, including a memory and a processor, characterized in that the memory is used to store a program supporting the processor in performing the method described therein, and the processor is configured to execute the program stored in the memory.
[0010] The present invention discloses a computer-readable storage medium storing a computer program, characterized in that the computer program is executed by a processor to perform the steps of the method described thereon.
[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention solves the problem of obtaining optimal solutions in traditional empirical design, achieving automated design with multiple parameters. Currently, the design of multi-cavity parallel structures relies heavily on empirical design or manual parameter tuning, making it difficult to accurately obtain the optimal structural design parameters. This invention clarifies the key structural parameters affecting sound absorption performance as combinations of structural design variables, establishes a parameter optimization model with the goal of maximizing the average sound absorption coefficient within the target frequency band, and employs a particle swarm optimization algorithm to automatically iterate and optimize each structural design variable. This effectively solves the design problem under multi-variable and strongly coupled conditions, improving design quality and efficiency.
[0012] 2. This invention significantly improves the sound absorption performance within the target frequency band, achieving broadband and efficient sound absorption. Addressing the problems of narrow sound absorption bandwidth and difficulty in low-frequency control inherent in traditional Helmholtz resonators, this invention employs a multi-cavity parallel collaborative sound absorption structure composed of n×n HRUs arranged in a preset array, based on an initial structural model. Compared to a periodic uniform structure, different HRUs have different structural parameters, resulting in different resonant frequencies for each unit. When they work collaboratively, they can couple multiple adjacent narrow-band sound absorption peaks, forming a broadband sound absorption effect.
[0013] 3. This invention provides a reliable optimization design method by coupling simulation and optimization algorithms. It establishes an iterative optimization method between a finite element simulation model and a particle swarm optimization algorithm. The combination of structural design variables corresponding to the current position of the particle is input into the finite element simulation model to calculate the sound absorption coefficient, and the obtained optimization results are transmitted back to the simulation model for simulation verification. This closed-loop optimization design ensures that each update of the structural design variable combination can be verified by the simulation model, making the entire process reliable. Attached Figure Description
[0014] Figure 1 This is a schematic diagram of the specific structure of a single HRU in this invention; Figure 2 This is a schematic diagram of the initial 4×4 structure of the multi-cavity parallel synergistic sound-absorbing metamaterial of the present invention; Figure 3 This is a schematic diagram of the finite element simulation model of a single HRU of the present invention; Figure 4 This is a schematic diagram of the finite element simulation model of the multi-cavity parallel synergistic sound-absorbing metamaterial of the present invention; Figure 5 This is a graph showing the sound absorption coefficient of a single HRU before optimization in this invention. Figure 6 This is a graph showing the sound absorption coefficient of the multi-cavity parallel synergistic sound-absorbing metamaterial before optimization. Figure 7 This is a flowchart illustrating the structural parameter optimization process based on the particle swarm optimization algorithm of this invention. Detailed Implementation
[0015] The present invention will be further described below with reference to the accompanying drawings and specific analytical examples.
[0016] In this embodiment, an optimization design method for a multi-cavity parallel synergistic sound-absorbing metamaterial structure is presented. This method, based on finite element analysis and parameter optimization algorithms, optimizes the multi-cavity parallel synergistic sound-absorbing metamaterial structure and includes the following steps: Step 1: Construct the initial structural model of the multi-cavity parallel synergistic sound-absorbing metamaterial. The initial structural model consists of n×n non-uniform Helmholtz resonant units (HRUs) arranged in a preset array. Each HRU consists of a cavity and an extended neck connected to it. The structural parameters of different HRUs are different, thus forming a non-uniform structure.
[0017] like Figure 1 As shown, a single HRU is the basic building block of a multi-cavity parallel synergistic sound-absorbing metamaterial structure. Figure 1 middle, E represents the radius of the extended neck; E is the length of the extended neck. This refers to the radius of the cavity. The HRU includes a cavity and an extended neck connected to the cavity. When sound waves pass through this unit, the air inside the extended neck and the air inside the cavity together form a resonant sound-absorbing structure, achieving sound absorption through resonance. The frequency at which resonance occurs is influenced by the combined effects of the extended neck radius, the extended neck length, and the cavity radius.
[0018] for Figure 2 The initial 4×4 structure shown has an overall square cross-section of 80mm×80mm. The extended neck radius, extended neck length, and cavity radius of each unit are adjusted as structural design variables, with the cavity radius... ∈[7.0,9.0]mm, ∈[1.0,5.0]mm, E∈(0,45]mm, the thickness of the entire structure is 80mm. The resonant frequency can be adjusted by adjusting the length of the extended neck, the radius of the extended neck, and the radius of the cavity.
[0019] Step 2: Establish the finite element simulation model corresponding to the initial structural model, and use the structural parameters of HRU as structural design variables, including: cavity length, extended neck length and extended neck radius. Analyze the sound absorption performance of the finite element simulation model under different structural design variables to obtain the corresponding sound absorption coefficient curve and sound absorption results in the target frequency band.
[0020] Step 2.1: Establish a finite element simulation model of the multi-cavity parallel synergistic sound-absorbing metamaterial structure in COMSOL software; Step 2.2: Set the structural design variables for the finite element simulation model. The range of structural design variables is determined by structural size limitations, manufacturing constraints, and target frequency band requirements. like Figure 3 As shown, a finite element simulation model of a single HRU was established in COMSOL software to analyze its sound absorption performance. The structural design variable was set as an increased neck radius. =1.8mm, extended neck length E=32.8mm, extended neck wall thickness is 0.6mm, air zone is 10×10×50mm, and layering is set 10mm from the top.
[0021] Based on the single HRU finite element model, a finite element model of a multi-cavity parallel synergistic sound-absorbing metamaterial is further established. For example... Figure 4 As shown, a parallel structure consisting of 4×4 HRUs is established, and the extended neck radius is set. =1.0 5.0mm, extended neck length E=0 45mm, cavity radius =7~9mm, air area is 80×80×50mm, and layering is set 10mm from the top.
[0022] Step 2.3: Set the plane wave to be incident along the direction directly opposite the HRU, and after setting a perfectly matched layer on the top surface of the HRU, mesh the finite element simulation model, and then calculate the sound absorption performance of the meshed finite element simulation model in the target frequency band to obtain the corresponding sound absorption coefficient curve and the sound absorption results in the target frequency band.
[0023] After the parametric model is established, the material property of the entire model is defined as air. Pressure, sound pressure, and other parameters in the pressure acoustic frequency domain are set, boundary conditions are defined, a background pressure field is added, and the direction of sound wave propagation and radiation type are determined. Based on the research, a mesh is generated and calculations are performed.
[0024] For a single HRU finite element simulation model, a probe was added at the 10mm layer to observe the sound absorption of the HRU. The sound absorption coefficient curve is shown below. Figure 5 As shown in the figure. The results indicate that the sound absorption coefficient increases significantly near the resonant frequency, while it remains relatively low in frequency bands far from the resonant frequency.
[0025] For a parallel structure model consisting of 4×4 HRUs, the sound absorption of the model was observed by adding probes at the 10mm layer. The sound absorption coefficient curve is shown below. Figure 6 As shown in the figure, the results indicate that there are multiple peaks, mainly because the simulation model is a combination of the structural parameters of each HRU. Therefore, in order to further improve the overall sound absorption performance in the target frequency band, parameter optimization is required.
[0026] Step 3: Input the structural design variables corresponding to the current position of the particle into the finite element simulation model, and then use equation (1) to calculate the sound absorption coefficient in the target frequency band. And with the highest average sound absorption coefficient within the target frequency band To optimize the objective, a particle swarm optimization algorithm is used to iteratively optimize the structural design variables of each particle. The optimization results are then passed to a finite element simulation model for simulation until a preset termination condition is met, thus obtaining the optimal structural design variables. (1) In equation (1), This represents the total number of discrete points within the target frequency band. This represents the frequency of the k-th discrete point. yes The sound absorption coefficient at that location.
[0027] In step 3, the optimization objective for the structural design variable is to maximize the average sound absorption coefficient within the target frequency band. Let m discrete points be obtained from the COMSOL simulation analysis within the target frequency band, denoted as... Suppose that the multi-cavity parallel synergistic sound-absorbing metamaterial consists of N HRUs, then according to equation (1), the combination of structural design variables for each unit can be expressed. for: (1) In equation (1), This represents the extended neck radius of the i-th HRU. This represents the extended neck length of the i-th HRU.
[0028] For the i-th HRU, its frequency is denoted according to equation (2). Surface impedance at for: (2) In equation (2), This represents the cavity length of the i-th HRU.
[0029] After multiple HRUs are connected in parallel, the equivalent surface impedance is calculated using equation (3). : (3) In equation (3), Let i be the area of the i-th HRU. For multi-cavity parallel synergistic sound-absorbing metamaterials at frequencies The equivalent surface impedance at that location.
[0030] The frequency is calculated using equation (4) based on the equivalent surface impedance. sound absorption coefficient at : (4) In equation (4), Let be the real part of the equivalent surface impedance. This is the imaginary part of the equivalent surface impedance.
[0031] To evaluate the sound absorption and energy absorption within the target frequency band, the average sound absorption coefficient within the target frequency band is calculated using equation (5). : (5) In equation (5), This represents the total number of discrete points within the target frequency band. yes The sound absorption coefficient at that location.
[0032] Based on equation (6), a parameter optimization model is established with the objective of maximizing the average sound absorption coefficient within the target frequency band: (6) Since particle swarm optimization typically solves by minimizing the objective function, the above optimization model can be equivalently represented by equation (7) as follows: (7) In equation (7), .
[0033] By establishing the above objective function, the sound absorption performance of each parameter combination can be transformed into an evaluation index for the particle swarm optimization algorithm.
[0034] After establishing the objective function, a structural optimization program is written in MATLAB, which uses the extended neck radius, extended neck length, and cavity radius as the combination of optimization design variables. The variable combination is updated according to the optimization results, and the finite element simulation model is called to perform calculations. The design variables were iteratively optimized using the particle swarm optimization algorithm in MATLAB software. For example... Figure 7 The diagram illustrates the optimization process of the particle swarm optimization (PSO) algorithm. First, the range of values for the structural design variables is set and initialized. The particle swarm size is set to 200, and the maximum number of iterations is also set to 200. Then, the PSO algorithm iterates. The PSO algorithm generates candidate combinations of structural design variables based on particle velocity and position update strategies, and determines whether the preset conditions are met through the coupling results of MATLAB and COMSOL. After multiple iterations to optimize the sound absorption coefficient, the PSO algorithm terminates when the change in the objective function value is less than the threshold of 0.001 or when the set number of iterations (200) is reached, outputting the final combination of structural design variables.
[0035] Based on this combination, an optimized design scheme for a multi-cavity parallel synergistic sound-absorbing metamaterial can be obtained. The optimized parameter combination is then passed to COMSOL for simulation analysis and verification, yielding the sound absorption coefficient curve and average sound absorption coefficient of the optimized structure in the target frequency band.
[0036] During the optimization process, MATLAB first provides an initial combination of structural design variables and passes it to COMSOL for simulation analysis. Subsequently, COMSOL returns the obtained sound absorption coefficient results to MATLAB, which calculates the objective function value based on these results and evaluates the current combination to determine if it is the optimal combination of structural design variables. If the preset termination condition is not met, MATLAB updates the combination according to the optimization algorithm and passes it back to COMSOL for simulation analysis; this process is iterated continuously until the optimal solution is found.
[0037] Step 4: Based on the optimal structural design variables, obtain the design scheme of the multi-cavity parallel synergistic sound-absorbing metamaterial structure.
[0038] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.
[0039] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.
Claims
1. A method for the optimal design of a multi-cavity parallel cooperative sound absorbing metamaterial structure, characterized by, Includes the following steps: Step 1: Construct an initial structural model of a multi-cavity parallel synergistic sound-absorbing metamaterial. The initial structural model consists of n×n non-uniform Helmholtz resonant units (HRUs) arranged in a preset array. Each HRU consists of a cavity and an extended neck connected to it. The structural parameters of different HRUs are different, thus forming a non-uniform structure. Step 2: Establish the finite element simulation model corresponding to the initial structural model, and use the structural parameters of HRU as structural design variables, including: cavity length, extended neck length and extended neck radius. Analyze the sound absorption performance of the finite element simulation model under different structural design variables to obtain the corresponding sound absorption coefficient curve and sound absorption results in the target frequency band. Step 3: Input the structural design variables corresponding to the current position of the particle into the finite element simulation model to calculate the sound absorption coefficient in the target frequency band. With the maximum average sound absorption coefficient in the target frequency band as the optimization objective, the particle swarm optimization algorithm is used to iteratively optimize the structural design variables of each particle. The optimization results are then transmitted to the finite element simulation model for simulation until the preset termination condition is met, and the optimal structural design variables are obtained. Step 4: Based on the optimal structural design variables, obtain the design scheme of the multi-cavity parallel synergistic sound-absorbing metamaterial structure.
2. The method for the optimal design of a multi-cavity parallel collaborative sound absorption metamaterial structure according to claim 1, characterized in that, Step 2 includes the following steps: Step 2.1: Establish a finite element simulation model of the multi-cavity parallel synergistic sound-absorbing metamaterial structure in COMSOL software; Step 2.2: Set the structural design variables for the finite element simulation model. The range of the structural design variables is determined by structural size limitations, manufacturing constraints, and target frequency band requirements. Step 2.3: Set the plane wave to be incident along the direction directly opposite the HRU, and after setting a perfectly matched layer on the top surface of the HRU, mesh the finite element simulation model, and then calculate the sound absorption performance of the meshed finite element simulation model in the target frequency band to obtain the corresponding sound absorption coefficient curve and the sound absorption results in the target frequency band.
3. The optimized design method for a multi-cavity parallel synergistic sound-absorbing metamaterial structure according to claim 1, characterized in that, Step 3 involves using equation (1) to calculate the average sound absorption coefficient within the target frequency band. : (1) In equation (1), This represents the total number of discrete points within the target frequency band. This represents the frequency of the k-th discrete point. yes The sound absorption coefficient at that location.
4. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports a processor in executing the method of any one of claims 1-3, the processor being configured to execute the program stored in the memory.
5. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program is executed by the processor to perform the steps of the method according to any one of claims 1-3.