Prediction Method for Absorption Coefficient of Helmholtz Resonance-Type Acoustic Metastructure

Abstract

A prediction method for the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure, which relates to the field of acoustics. The prediction method for the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure includes the following steps: Select multiple groups of structural parameter data of the acoustic superstructure for numerical simulation to obtain multiple groups of sound absorption coefficient data; According to the obtained sound absorption coefficient data, fit the sound absorption coefficient curve through an asymmetric distribution function by the least squares method, and determine the undetermined coefficients of the asymmetric distribution function according to the fitted sound absorption coefficient curve; Construct a surrogate model based on the response surface function to reflect the mapping relationship between each structural parameter data and the undetermined coefficients of the asymmetric distribution function; Predict the sound absorption coefficient of the acoustic superstructure according to the surrogate model. The prediction method for the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure can greatly reduce the computational resources and time costs, quickly fit the sound absorption coefficient curve and construct a surrogate model with low prediction time cost to predict the sound absorption coefficient of the acoustic superstructure.

Description

Technical Field

[0001] This application relates to the field of acoustics. Specifically, it relates to a method for predicting the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure. Background Technique

[0002] Traditional sound absorption or sound insulation materials often only have good noise reduction effects on medium and high frequency noises, and are often "incapable" when facing low frequency noises. In recent years, the problem of low frequency noise pollution has become increasingly serious, and the research on acoustic superstructures with good noise reduction effects on low frequency noises has also become increasingly popular. An acoustic superstructure is an artificially designed structure with many special properties different from traditional sound absorption materials, such as negative mass density, negative elastic modulus, etc. Deforming the structure of a Helmholtz resonator or combining it with different materials can form various Helmholtz resonance type acoustic superstructures, which have good noise reduction effects on low frequency noises. Traditional sound absorption materials such as microperforated plates and porous materials are restricted by the mass action law and often require relatively thick structural dimensions to have good noise reduction effects on low frequency noises. Acoustic superstructures are different. Since they break through the constraints of the mass action law, they can control low frequency noises with larger wavelengths with only smaller structural dimensions, and are expected to solve the problem of low frequency noise reduction.

[0003] The sound absorption coefficient is one of the important indicators for evaluating the quality of noise reduction effects. At a certain frequency, the closer the sound absorption coefficient is to 1, the greater the dissipation of sound energy by the acoustic superstructure, and the better the noise reduction effect.

[0004] The current existing design process of Helmholtz resonance type acoustic superstructures is usually as follows: First, an analytical model of the sound absorption coefficient is established through acoustic principles, then numerical simulation or drawing of the sound absorption coefficient curve is carried out with the help of simulation software or programming software, and finally experimental verification is carried out with the help of an impedance tube experiment. In the above design method, although the cost of optimizing the design of the sound absorption coefficient based on the analytical model is relatively low, the derivation of the formula requires high mathematical calculation ability. Although the method based on numerical simulation can also calculate and optimize the sound absorption coefficient, it usually takes a very long time to calculate and occupies a large amount of computing resources; in addition, the above is usually only applicable to structures with regular or relatively simple structures, because it is usually difficult to construct an analytical model for irregular or complex structures, and it is more difficult to guarantee the prediction accuracy. Summary of the Invention

[0005] The purpose of this application is to provide a method for predicting the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure, which can greatly reduce the computing resources and time cost, quickly fit the sound absorption coefficient curve and construct a surrogate model with a lower prediction time cost to predict the sound absorption coefficient of the acoustic superstructure.

[0006] This application is implemented as follows:

[0007] This application provides a method for predicting the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure, including the following steps:

[0008] Select multiple groups of structural parameter data of the acoustic superstructure for numerical simulation to obtain multiple groups of sound absorption coefficient data;

[0009] Based on the obtained sound absorption coefficient data, fit the sound absorption coefficient curve through an asymmetric distribution function using the least squares method, and determine the undetermined coefficients of the asymmetric distribution function according to the fitted sound absorption coefficient curve;

[0010] Construct a surrogate model based on the response surface function to reflect the mapping relationship between each structural parameter data and the undetermined coefficients of the asymmetric distribution function;

[0011] Predict the sound absorption coefficient of the acoustic superstructure according to the surrogate model.

[0012] In some alternative implementation schemes, when fitting the sound absorption coefficient curve through an asymmetric distribution function based on the obtained sound absorption coefficient data using the least squares method, the following asymmetric distribution function is adopted:

[0013]

[0014] where A, B, ε 1 、ε 2 are all undetermined coefficients of the asymmetric distribution function, x is the independent variable of the asymmetric distribution function, representing the calculation frequency when obtaining simulation data; f(x) is the output value of the asymmetric distribution function.

[0015] In some alternative implementation schemes, when fitting the sound absorption coefficient curve through an asymmetric distribution function based on the obtained sound absorption coefficient data using the least squares method, the part where the sound absorption coefficient is greater than 0.2 is fitted.

[0016] In some alternative implementation schemes, when constructing a surrogate model based on the response surface function to reflect the mapping relationship between each structural parameter data and each undetermined coefficient, the following response surface function is adopted:

[0017]

[0018] where a 0 、a i 、a ii 、a ij are the undetermined coefficients of the response surface function respectively, x i 、x j represent the structural parameter data; y is the output value of the response surface function.

[0019] In some alternative embodiments, after constructing a surrogate model that maps the data of each structural parameter of the reaction and the undetermined coefficients of the asymmetric distribution function based on the response surface function, the normalized root mean square error between the output value of each response surface function and each undetermined coefficient value of the asymmetric distribution function is calculated respectively, and the accuracy of the response surface function is judged according to the normalized root mean square error.

[0020] In some alternative embodiments, the structural parameter data of each group of acoustic superstructures includes the data of at least two structural parameters.

[0021] In some alternative embodiments, when obtaining multiple groups of sound absorption coefficient data through numerical simulation of the structural parameter data of multiple groups of acoustic superstructures, the required frequency and step size are set.

[0022] The beneficial effects of this application are as follows: The method for predicting the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by this application includes the following steps: Selecting the structural parameter data of multiple groups of acoustic superstructures for numerical simulation to obtain multiple groups of sound absorption coefficient data; Fitting the sound absorption coefficient curve based on the least squares method through an asymmetric distribution function according to the obtained sound absorption coefficient data, and determining the undetermined coefficients of the asymmetric distribution function according to the fitted sound absorption coefficient curve; Constructing a surrogate model that maps the data of each structural parameter and the undetermined coefficients of the asymmetric distribution function based on the response surface function; Predicting the sound absorption coefficient of the acoustic superstructure according to the surrogate model. The method for predicting the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by this application can greatly reduce the computational resources and time costs, quickly fit the sound absorption coefficient curve and construct a surrogate model with a lower prediction time cost to predict the sound absorption coefficient of the acoustic superstructure. Description of the Drawings

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

[0024] Figure 1 It is a schematic cross-sectional structure diagram of the simulation structure of the Helmholtz resonance type acoustic superstructure predicted by the method for predicting the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of this application;

[0025] Figure 2 It is a schematic diagram of the conical neck in the simulation structure of the Helmholtz resonance type acoustic superstructure predicted by the method for predicting the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of this application;

[0026] Figure 3 Schematic cross-sectional structure diagram of the conical neck in the simulation structure of the Helmholtz resonance type acoustic superstructure predicted by the prediction method of the absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application;

[0027] Figure 4 Absorption coefficient curve characteristics of W-NETHR with different structural parameters in the prediction method of the absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application;

[0028] Figure 5 Fitting effect of the absorption coefficient in the fifth group of schemes of the orthogonal design in the prediction method of the absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application;

[0029] Figure 6 Residual of the absorption coefficient in the fifth group of schemes of the orthogonal design in the prediction method of the absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application;

[0030] Figure 7 Fitting effect of the absorption coefficient in the eighth group of schemes of the orthogonal design in the prediction method of the absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application;

[0031] Figure 8 Residual of the absorption coefficient in the eighth group of schemes of the orthogonal design in the prediction method of the absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application;

[0032] Figure 9 Fitting effect of the absorption coefficient in the twelfth group of schemes of the orthogonal design in the prediction method of the absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application;

[0033] Figure 10 Residual of the absorption coefficient in the twelfth group of schemes of the orthogonal design in the prediction method of the absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application;

[0034] Figure 11 Fitting effect of the absorption coefficient in the twenty-fourth group of schemes of the orthogonal design in the prediction method of the absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application;

[0035] Figure 12 Residual of the absorption coefficient in the twenty-fourth group of schemes of the orthogonal design in the prediction method of the absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application;

[0036] Figure 13The relative error of the response surface function with the undetermined coefficient A of the asymmetric distribution function as the output in the prediction method of the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application;

[0037] Figure 14 The relative error of the response surface function with the undetermined coefficient B of the asymmetric distribution function as the output in the prediction method of the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application;

[0038] Figure 15 The relative error of the response surface function with the undetermined coefficient ε of the asymmetric distribution function as the output in the prediction method of the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application 1 as the output;

[0039] Figure 16 The relative error of the response surface function with the undetermined coefficient ε of the asymmetric distribution function as the output in the prediction method of the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application 2 as the output;

[0040] Figure 17 The values of the five structural parameters input in the prediction method of the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application are d n1 = 7 mm, d n2 = 4 mm, l n = 30 mm, b = 4 mm, δ = 0.6 mm, and the predicted results of the sound absorption coefficient are obtained. Detailed implementation manners

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Usually, the components of the embodiments of the present application described and illustrated herein can be arranged and designed in various different configurations.

[0042] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application claimed, but merely represents the selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the scope of protection of the present application.

[0043] The embodiment of the present application provides a prediction method for the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure, including the following steps:

[0044] Step 1: Select multiple groups of structural parameter data of the acoustic metamaterial and perform numerical simulations using finite element simulation software to obtain multiple groups of sound absorption coefficient data. Optionally, each group of structural parameter data of the acoustic metamaterial includes data of at least two structural parameters. Optionally, set the required frequency and step size when performing numerical simulations using finite element simulation software.

[0045] Step 2: Based on the obtained sound absorption coefficient data, fit the sound absorption coefficient curve using the least squares method through an asymmetric distribution function, and determine the undetermined coefficients of the asymmetric distribution function according to the fitted sound absorption coefficient curve. Optionally, when fitting the sound absorption coefficient curve, use the following asymmetric distribution function:

[0046]

[0047] In the formula, A, B, ε 1 , ε 2 are all undetermined coefficients of the asymmetric distribution function, x is the independent variable of the asymmetric distribution function, representing the calculation frequency when obtaining simulation data; f(x) is the output value of the asymmetric distribution function.

[0048] Optionally, when fitting the sound absorption coefficient curve, fit the part where the sound absorption coefficient is greater than 0.2.

[0049] Step 3: Construct a surrogate model based on the response surface function to reflect the mapping relationship between each group of structural parameter data and the undetermined coefficients of the asymmetric distribution function. Optionally, use the following response surface function:

[0050]

[0051] In the formula, a 0 , a i , a ii , a ij are the undetermined coefficients of the response surface function respectively, x i , x j represent the structural parameter data; y is the output value of the response surface function.

[0052] Optionally, after constructing a surrogate model based on the response surface function to reflect the mapping relationship between each group of structural parameter data and the undetermined coefficients of the asymmetric distribution function, calculate the normalized root mean square error between the output value of each response surface function and each undetermined coefficient value of the asymmetric distribution function respectively, and judge the accuracy of the response surface function according to the normalized root mean square error.

[0053] Step 4: Predict the sound absorption coefficient of the acoustic metamaterial according to the surrogate model.

[0054] The prediction method of the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the embodiment of the present application obtains multiple groups of sound absorption coefficient data through numerical simulation using finite element simulation software by selecting multiple groups of structural parameter data of the acoustic superstructure. Subsequently, based on the obtained sound absorption coefficient data, the sound absorption coefficient curve is fitted by an asymmetric distribution function based on the least squares method, and the undetermined coefficients of the asymmetric distribution function are determined according to the fitted sound absorption coefficient curve. Then, a surrogate model reflecting the mapping relationship between each structural parameter data and the undetermined coefficients of the asymmetric distribution function is constructed based on the response surface function. Finally, the sound absorption coefficient of the acoustic superstructure is predicted according to the surrogate model. The prediction method of the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the present application can well fit the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure by introducing an asymmetric distribution function. At the same time, the mapping relationship between input and output is constructed based on the response surface function, which has the advantages of strong selectivity and strong applicability. On the premise that the number of tests is not less than the number of undetermined coefficients of the response surface function, the experimental method, experimental scheme and response surface function can be independently selected according to the actual situation, so as to greatly reduce the computing resources and time cost, quickly fit the sound absorption coefficient curve and construct a surrogate model with a lower prediction time cost to predict the sound absorption coefficient of the acoustic superstructure, which is convenient for later optimization design.

[0055] The characteristics and performance of the prediction method of the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure of the present application are further described in detail below in conjunction with the embodiments.

[0056] Embodiment 1

[0057] The embodiment of the present application provides a prediction method for the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure, and predicts the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure W-NETHR as shown in Figure 1 、 Figure 2 and Figure 3 . The Helmholtz resonance type acoustic superstructure includes a hollow cylinder and a conical neck arranged in the cylinder. The conical neck is a hollow frustum-shaped structure, and the end with a larger diameter is coaxially connected to the inner wall of one end of the cylinder. The conical neck has a smooth wavy outer wall, and the wavy outer wall of the conical neck is composed of annular protrusions and annular depressions arranged at intervals along its axial direction; the prediction method includes the following steps:

[0058] Step 1: Select five structural parameters of the acoustic superstructure: the inlet diameter d n1 mm of the upper surface of the conical neck, the outlet diameter d n2 mm of the lower surface of the conical neck, the height l n mm of the conical neck, the wavelength b mm of the wavy outer wall of the conical neck, and the amplitude δ mm of the wavy outer wall of the conical neck; in other embodiments, the cylinder thickness t mm and the inner diameter r cmm, outer diameter r of the cylinder A Structural parameters such as mm and the length L mm of the cylinder. In this embodiment, five structural parameters of the acoustic superstructure are selected. In other embodiments, different structural parameters can be considered for optimization design according to specific problems.

[0059] In the embodiment of the present application, the number of simulation tests is limited by the number of undetermined coefficients in the response surface function during the later construction of the surrogate model. The selection of the response surface function is closely related to the number of structural parameters. The "order" of the response surface function is equal to the number of structural parameters, and the "degree" of the response surface function can be selected arbitrarily. Since there are 5 structural parameters considered in this embodiment, a five-variable quadratic response surface function is adopted. And there are 21 undetermined coefficients in the five-variable quadratic response surface function. Therefore, the number of simulation experiments needs to be no less than 21 times. Considering comprehensively, as shown in Table 1, in this embodiment, 5 level factors are taken for each structural parameter for combination to obtain the final orthogonal experimental scheme as shown in Table 2, with a total of 25 groups of structural parameter data. It should be emphasized again here that in practical applications, it is not limited to taking 5 level structural parameters for each structural parameter. It can be more than 5 or less than 5, which depends on the specific situation. This is just an example.

[0060] Table 1 Factors and levels of structural parameters

[0061]

[0062] Table 2 Data of each group of structural parameters in the orthogonal experimental scheme

[0063]

[0064]

[0065] Perform numerical simulation on the 25 groups of structural parameter data in the above orthogonal experimental scheme using the finite element simulation software COMSOL Multiphysics. When performing numerical simulation, set the frequency of the sound wave to be from 50 Hz to 800 Hz, with a step size of 2 Hz, to obtain 25 groups of sound absorption coefficient data. Some of the sound absorption coefficient curves are as Figure 4 shown.

[0066] Step 2: Based on the obtained sound absorption coefficient data, fit the sound absorption coefficient curve through the following asymmetric distribution function using the least squares method, and determine the undetermined coefficients of the asymmetric distribution function according to the fitted sound absorption coefficient curve:

[0067]

[0068] In the formula, A, B, ε 1 、ε 2They are all undetermined coefficients of the asymmetric distribution function. x is the independent variable of the asymmetric distribution function, representing the calculation frequency when obtaining simulation data; f(x) is the output value of the asymmetric distribution function.

[0069] When fitting the sound absorption coefficient curve through the above asymmetric distribution function based on the principle of the least squares method, only the part where the sound absorption coefficient is greater than 0.2 is fitted, aiming to improve the fitting accuracy. The undetermined coefficient values (A, B, ε 1 、ε 2 ) of the 25 groups of asymmetric distribution coefficients obtained after fitting are shown in Table 3. Four representative groups of data are selected for display as Figure 5 、 Figure 6 、 Figure 7 、 Figure 8 、 Figure 9 、 Figure 10 、 Figure 11 、 Figure 12 shown.

[0070] Table 3 Undetermined coefficient values of the asymmetric distribution function obtained after fitting

[0071]

[0072]

[0073] From Figure 6 、 Figure 8 、 Figure 10 、 Figure 12 's residual plots, it can be seen that the fitting results of these 25 groups of data are good, the fitting accuracy is high, the maximum residual does not exceed 0.15, and most of the residuals are below 0.1.

[0074] Step 3: Construct a surrogate model that reflects the mapping relationship between the data of each structural parameter and the undetermined coefficients of the asymmetric distribution function based on the following response surface function:

[0075]

[0076] In the formula, a 0 、a i 、a ii 、a ij are respectively the undetermined coefficients of the response surface function, and for the convenience of expression, they are denoted as a 0 、a 1 ……a 20 , that is, there are a total of 21 undetermined coefficients; x i 、x j represent the structural parameter data, which are respectively the inlet diameter d n1 of the upper surface of the conical neck, the outlet diameter d n2 of the lower surface of the conical neck, and the height l n of the conical neck, the wavelength b of the conical neck corrugated outer wall, the amplitude δ of the conical neck corrugated outer wall; y is the output value of the response surface function.

[0077] Based on the least squares method, a mapping relationship between the structural parameters and the four undetermined coefficients of the asymmetric distribution function is constructed through the above response surface function, corresponding to the four undetermined coefficients (A, B, ε 1 , ε 2 ) of the asymmetric distribution function, and four response surface functions are obtained respectively. The undetermined coefficients (a 0 , a 1 ... a 20 ) of each response surface function are shown in Table 4.

[0078] Table 4 Undetermined coefficients of four groups of response surface functions

[0079]

[0080] To evaluate the accuracy of the four obtained response surface functions, in this embodiment, the relative error and the normalized root mean square error between the output y of the four response surface functions and the four undetermined coefficient values (A, B, ε 1 , ε 2 ) of the asymmetric distribution function obtained in Step 2 are calculated respectively. The relative errors are shown in Figure 13 , Figure 14 , Figure 15 and Figure 16 , and the normalized root mean square error is shown in Table 5.

[0081] Table 5 Normalized root mean square error

[0082]

[0083]

[0084] The normalized root mean square error is an index used to measure the performance of a prediction model. By calculating the normalized root mean square error, a value between 0 and 1 can be obtained. The smaller the normalized root mean square error, the better the fitting degree of the prediction model. It can be seen from Table 5 that the fitting degrees of the four response surface functions are all good.

[0085] Step 4: Predict the sound absorption coefficient of the acoustic superstructure according to the surrogate model.

[0086] After constructing the surrogate model, by changing the values of the five input structural parameters, the undetermined coefficients A, B, ε 1 , ε 2 of the corresponding output asymmetric distribution function can be obtained, and then the sound absorption coefficient can be predicted by drawing a curve according to the asymmetric distribution function.

[0087] For example, change the values of the five input structural parameters to: d n1= 7 mm, d n2 = 4 mm, l n = 30 mm, b = 4 mm, δ = 0.6 mm, the predicted results of the sound absorption coefficient are as Figure 17 shown. The abscissa is the acoustic wave frequency, and the ordinate is the sound absorption coefficient. Then, the accuracy of the predicted results can be verified through simulation. If the simulation results are in good agreement with the predicted results, it indicates that the surrogate model is successfully constructed. Otherwise, it is necessary to increase the number of experiments or select different response surface functions according to the specific situation. The prediction time cost (calculating a single acoustic superstructure) of the sound absorption coefficient prediction method for the Helmholtz resonance type acoustic superstructure provided in the embodiment of the present application is shown in Table 6.

[0088] Table 6 Prediction Time

[0089]

[0090] The embodiments described above are some, but not all, of the embodiments of the present application. The detailed description of the embodiments of the present application is not intended to limit the scope of the present application claimed, but merely represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without making creative efforts fall within the scope of protection of the present application.

Claims

1. A method for predicting the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure, characterized in that: The following steps are involved: Select multiple sets of structural parameter data of acoustic superstructures for numerical simulation to obtain multiple sets of sound absorption coefficient data; According to the acquired sound absorption coefficient data, a sound absorption coefficient curve is fitted by an asymmetric distribution function based on the least squares method, and the undetermined coefficient of the asymmetric distribution function is determined according to the fitted sound absorption coefficient curve; when fitting the sound absorption coefficient curve by an asymmetric distribution function based on the least squares method according to the acquired sound absorption coefficient data, the following asymmetric distribution function is used: In the formula, A, B, ε1, ε2 are all unknown coefficients of the asymmetric distribution function, x is the independent variable of the asymmetric distribution function, representing the calculation frequency when obtaining simulation data; f(x) is the output value of the asymmetric distribution function; Constructing a proxy model based on a response surface function to reflect the mapping relationship between each of the structural parameter data and the undetermined coefficients of the asymmetric distribution function; A sound absorption coefficient of the acoustic superstructure is predicted based on the proxy model.

2. The method for predicting the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure according to claim 1, characterized in that: When fitting the sound absorption coefficient curve through an asymmetric distribution function based on the least square method according to the acquired sound absorption coefficient data, the part with a sound absorption coefficient greater than 0.2 is fitted.

3. The method for predicting the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure according to claim 1, characterized in that: When constructing a proxy model based on a response surface function to reflect the mapping relationship between each of the structural parameter data and each of the undetermined coefficients, the following response surface function is used: In the formula, a0, a i 、a ii 、a ij are the unknown coefficients of the response surface function, x i 、x j represents the structural parameter data; y is the output value of the response surface function.

4. The method for predicting the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure according to claim 1, characterized in that: After constructing a proxy model reflecting the mapping relationship between each of the structural parameter data and the undetermined coefficients of the asymmetric distribution function based on the response surface function, the normalized root mean square errors between the output values ​​of each response surface function and each undetermined coefficient value of the asymmetric distribution function are calculated respectively, and the accuracy of the response surface function is judged according to the normalized root mean square error.

5. The method for predicting the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure according to claim 1, characterized in that: Each set of structural parameter data of the acoustic superstructure includes data of at least two structural parameters.

6. The method for predicting the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure according to claim 1, characterized in that: When multiple sets of structural parameter data of acoustic superstructures are selected for numerical simulation to obtain multiple sets of sound absorption coefficient data, the required frequency and step size are set.

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