Prediction method for sound absorption coefficient of Helmholtz resonance type acoustic superstructure

Abstract

The invention discloses a method for predicting the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure, and relates to the field of acoustics. The method for predicting the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure comprises the following steps: selecting multiple groups of structural parameter data of the acoustic superstructure, and carrying out numerical simulation to obtain multiple groups of sound absorption coefficient data; according to the acquired sound absorption coefficient data, fitting a sound absorption coefficient curve through an asymmetric distribution function by a least square method, and determining an undetermined coefficient of the asymmetric distribution function according to the fitted sound absorption coefficient curve; constructing an agent model reflecting a mapping relation between the structural parameter data and undetermined coefficients of the asymmetric distribution function based on the response surface function; and predicting the sound absorption coefficient of the acoustic superstructure according to the proxy model. According to the method for predicting the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure, computing resources and time cost can be greatly reduced, a sound absorption coefficient curve is quickly fitted, and the sound absorption coefficient of the acoustic superstructure is predicted by constructing an agent model with low prediction time cost.

Description

Technical Field

[0001] The present application relates to the field of acoustics, and in particular to a method for predicting the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure. Background Art

[0002] Traditional sound-absorbing or sound-insulating materials often only have good noise reduction effects on medium and high frequency noises, and are often "powerless" 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 that have good noise reduction effects on low-frequency noises has also become more and more popular. Acoustic superstructures are artificially designed structures with many special properties that are different from traditional sound-absorbing materials, such as negative mass density and negative elastic modulus. By deforming the structure of the Helmholtz resonator or combining it with different materials, various Helmholtz resonance-type acoustic superstructures can be formed, which have good noise reduction effects on low-frequency noises. Traditional sound-absorbing materials such as micro-perforated plates and porous materials are subject to the law of mass action, and often require thicker structural dimensions to have a good noise reduction effect on low-frequency noise. Acoustic superstructures are not like this. Because they break through the constraints of the law of mass action, they only need smaller structural dimensions to control low-frequency noises with larger wavelengths, which is expected to solve the problem of low-frequency noise reduction.

[0003] The sound absorption coefficient is one of the important indicators for evaluating the noise reduction effect. 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 design process of the existing Helmholtz resonance acoustic superstructure is usually as follows: first, establish an analytical model of the sound absorption coefficient through acoustic principles, then use simulation software or programming software to perform numerical simulation or draw a sound absorption coefficient curve, and finally use impedance tube experiments to conduct experimental verification. In the above design methods, although the cost of optimizing the sound absorption coefficient based on the analytical model is relatively low, the derivation of the formula requires high mathematical computing power. Although the method based on numerical simulation can also calculate and optimize the sound absorption coefficient, the calculation is usually very time-consuming and occupies a lot of computing resources; in addition, the above is usually only applicable to regular or relatively simple structures, because the analytical model of irregular or complex structures is usually difficult to build, and the prediction accuracy is even more difficult to guarantee. Summary of the invention

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

[0006] This application is implemented as follows:

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

[0008] Select multiple sets of structural parameter data of acoustic superstructures for numerical simulation to obtain multiple sets of sound absorption coefficient data;

[0009] Fitting a sound absorption coefficient curve through an asymmetric distribution function based on the least square method according to the acquired sound absorption coefficient data, and determining the undetermined coefficient of the asymmetric distribution function according to the fitted sound absorption coefficient curve;

[0010] Based on the response surface function, a proxy model reflecting the mapping relationship between the data of various structural parameters and the undetermined coefficients of the asymmetric distribution function is constructed;

[0011] Predict the sound absorption coefficient of an acoustic superstructure based on a surrogate model.

[0012] In some optional implementation schemes, when fitting the sound absorption coefficient curve through 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:

[0013]

[0014] Where A, B, ε1, and 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; and f(x) is the output value of the asymmetric distribution function.

[0015] In some optional embodiments, 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 portion with a sound absorption coefficient greater than 0.2 is fitted.

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

[0017]

[0018] 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.

[0019] In some optional implementation schemes, after constructing a proxy model based on the response surface function that reflects the mapping relationship between various structural parameter data and the undetermined coefficients of the asymmetric distribution function, the normalized root mean square errors between the output values ​​of each response surface function and the various undetermined coefficient values ​​of the asymmetric distribution function are calculated respectively, and the accuracy of the response surface function is judged based on the normalized root mean square error.

[0020] In some optional embodiments, each set of structural parameter data of the acoustic superstructure includes data of at least two structural parameters.

[0021] In some optional implementation schemes, when multiple sets of structural parameter data of an acoustic superstructure are selected for numerical simulation to obtain multiple sets of sound absorption coefficient data, the required frequency and step size are set.

[0022] The beneficial effects of the present application are as follows: the prediction method of the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the present application comprises the following steps: selecting multiple groups of structural parameter data of the acoustic superstructure for numerical simulation to obtain multiple groups of sound absorption coefficient data; fitting the sound absorption coefficient curve through the asymmetric distribution function based on the least squares method according to the acquired sound absorption coefficient data, and determining the undetermined coefficients of the asymmetric distribution function according to the fitted sound absorption coefficient curve; constructing a proxy model reflecting the mapping relationship between each structural parameter data and the undetermined coefficients of the asymmetric distribution function based on the response surface function; and predicting the sound absorption coefficient of the acoustic superstructure according to the proxy model. The prediction method of the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided by the present application can greatly reduce the computing resources and time cost, quickly fit the sound absorption coefficient curve and construct a proxy model with low prediction time cost to predict the sound absorption coefficient of the acoustic superstructure. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0024] Figure 1 A schematic cross-sectional structure diagram of a simulated structure of a Helmholtz resonance type acoustic superstructure predicted by a method for predicting a sound absorption coefficient of a Helmholtz resonance type acoustic superstructure provided in an embodiment of the present application;

[0025] Figure 2 A schematic diagram of a cone neck in a simulation structure of a Helmholtz resonance type acoustic superstructure predicted by a method for predicting the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure provided in an embodiment of the present application;

[0026] Figure 3 A schematic diagram of the cross-sectional structure of a cone neck in a simulation structure of a Helmholtz resonance type acoustic superstructure predicted by a method for predicting the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure provided in an embodiment of the present application;

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

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

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

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

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

[0032] Fig. 9 The fitting effect of the sound absorption coefficient in the 12th group of orthogonal design schemes in the prediction method of the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided in the embodiment of the present application;

[0033] Fig.10 The residual of the sound absorption coefficient in the 12th group of orthogonal design schemes in the prediction method of the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided in the embodiment of the present application;

[0034] Fig.11 The fitting effect of the sound absorption coefficient in the 24th group of orthogonal design schemes in the prediction method of the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided in the embodiment of the present application;

[0035] Fig.12 The residual of the sound absorption coefficient in the 24th group of orthogonal design schemes in the prediction method of the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided in the embodiment of the present application;

[0036] Fig.13The relative error of the response surface function using 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 in the embodiment of the present application;

[0037] Fig.14 The relative error of the response surface function using 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 in the embodiment of the present application;

[0038] Fig.15 The relative error of the response surface function using the undetermined coefficient ε1 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 in the embodiment of the present application;

[0039] Fig.16 The relative error of the response surface function using the undetermined coefficient ε2 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 in the embodiment of the present application;

[0040] Fig.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 in the embodiment of the present application are d n1 =7mm, d n2 =4mm, l n The predicted results of the sound absorption coefficient are obtained when a=30mm, b=4mm, and δ=0.6mm. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here 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 for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

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

[0044] Step 1: Select multiple sets of structural parameter data of the acoustic superstructure and use finite element simulation software to perform numerical simulation to obtain multiple sets of sound absorption coefficient data; optionally, each set of structural parameter data of the acoustic superstructure includes data of at least two structural parameters. Optionally, use finite element simulation software to perform numerical simulation to set the required frequency and step size.

[0045] Step 2: Fit the sound absorption coefficient curve through an asymmetric distribution function based on the least squares method according to the acquired sound absorption coefficient data, and determine the undetermined coefficient of the asymmetric distribution function according to the fitted sound absorption coefficient curve; optionally, when fitting the sound absorption coefficient curve, the following asymmetric distribution function is used:

[0046]

[0047] Where A, B, ε1, and 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; and f(x) is the output value of the asymmetric distribution function.

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

[0049] Step 3: Based on the response surface function, a proxy model is constructed to reflect the mapping relationship between each structural parameter data and the undetermined coefficients of the asymmetric distribution function; optionally, the following response surface function is used:

[0050]

[0051] 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.

[0052] Optionally, after constructing a proxy model based on the response surface function that reflects the mapping relationship between each structural parameter data and the undetermined coefficients of the asymmetric distribution function, the normalized root mean square errors between the output values ​​of each response surface function and the values ​​of each undetermined coefficient of the asymmetric distribution function are calculated respectively, and the accuracy of the response surface function is judged based on the normalized root mean square error.

[0053] Step 4: Predict the sound absorption coefficient of the acoustic superstructure based on the proxy model.

[0054] The method for predicting the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure provided in an embodiment of the present application obtains multiple sets of sound absorption coefficient data by selecting multiple sets of structural parameter data of the acoustic superstructure and performing numerical simulation using finite element simulation software. Subsequently, a sound absorption coefficient curve is fitted through an asymmetric distribution function based on the least squares method according to the acquired sound absorption coefficient data, and the undetermined coefficients of the asymmetric distribution function are determined according to the fitted sound absorption coefficient curve. Subsequently, a proxy model that reflects the mapping relationship between each structural parameter data and the undetermined coefficients of the asymmetric distribution function is constructed based on a response surface function. Finally, the sound absorption coefficient of the acoustic superstructure is predicted according to the proxy model. The prediction method of the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure provided in the present application can well fit the sound absorption coefficient of the Helmholtz resonance type acoustic superstructure by introducing an asymmetric distribution function, and at the same time constructs a mapping relationship between input and output based on a response surface function, which has the advantages of strong selectivity and applicability. On the premise that the number of experiments is not less than the number of undetermined coefficients of the response surface function, the experimental method, experimental plan and response surface function can be independently selected according to the actual situation, thereby greatly reducing the computing resources and time cost, quickly fitting the sound absorption coefficient curve and constructing a proxy model with low 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 for 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] Example 1

[0057] The present application embodiment provides a method for predicting the sound absorption coefficient of a Helmholtz resonance type acoustic superstructure. Figure 1 , Figure 2 and Figure 3 The sound absorption coefficient of the Helmholtz resonance type acoustic superstructure W-NETHR shown in the figure is predicted, and the Helmholtz resonance type acoustic superstructure includes a hollow cylinder and a conical neck arranged in the cylinder, the conical neck is a hollow truncated cone structure, and the end with a large diameter is coaxially connected to the inner wall of one end of the cylinder, and 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 of the upper surface of the cone neck n1 mm, outlet diameter of cone neck bottom surface d n2 mm, cone neck height l n mm, the wavelength bmm of the tapered neck wavy outer wall, and the amplitude δmm of the tapered neck wavy outer wall; in other embodiments, the thickness tmm of the cylinder and the inner diameter r of the cylinder can also be selected. cmm, outer diameter of cylinder r A mm, cylinder length Lmm and other structural parameters. 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 when the proxy model is constructed later. The selection of the response surface function is closely related to the number of structural parameters. The number of "elements" of the response surface function is equal to the number of structural parameters. The number of "times" of the response surface function can be selected by itself. Since there are 5 structural parameters considered in this embodiment, a five-element quadratic response surface function is adopted, and there are 21 undetermined coefficients in the five-element quadratic response surface function, so the number of simulation experiments must be not less than 21 times. Comprehensive consideration, as shown in Table 1, this embodiment takes 5 horizontal factors for each structural parameter to combine and obtain the final orthogonal experimental scheme as shown in Table 2, with a total of 25 groups of structural parameter data. It needs to be emphasized again here that in practical applications, it is not limited to taking 5 horizontal structural parameters for each structural parameter, and it can be more than 5 or less than 5, depending on the specific situation. This is just an example.

[0060] Table 1 Factors and levels of structural parameters

[0061]

[0062] Table 2 Structural parameter data of each group of orthogonal experimental scheme

[0063]

[0064]

[0065] The 25 sets of structural parameter data in the above orthogonal experimental scheme were numerically simulated using the finite element simulation software COMSOL Multiphysics. During the numerical simulation, the frequency of the sound wave was set to 50 Hz to 800 Hz with a step size of 2 Hz, and 25 sets of sound absorption coefficient data were obtained, among which some sound absorption coefficient curves are shown in Figure 2. Figure 4 shown.

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

[0067]

[0068] Where A, B, ε1, and 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; and 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 least squares method, only the part with sound absorption coefficient greater than 0.2 is fitted, in order to improve the fitting accuracy. The undetermined coefficient values ​​(A, B, ε1, ε2) 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. Figure 5 , Figure 6 , Figure 7 , Figure 8 , Fig. 9 , Fig.10 , Fig.11 , Fig.12 shown.

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

[0071]

[0072]

[0073] Depend on Figure 6 , Figure 8 , Fig.10 , Fig.12 From the residual diagram in , we can see 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: Based on the following response surface function, a proxy model is constructed to reflect the mapping relationship between each structural parameter data and the undetermined coefficients of the asymmetric distribution function:

[0075]

[0076] In the formula, a0, a i 、a ii 、a ij are the unknown coefficients of the response surface function, which are denoted as a0, a1, ... a for the convenience of expression. 20 , that is, a total of 21 undetermined coefficients; x i 、x j Represents the structural parameter data, which are the inlet diameter d of the upper surface of the cone neck n1 , outlet diameter d of the lower surface of the cone neck n2 、Conical neck height l n , the wavelength b of the wavy outer wall of the cone neck, the amplitude δ of the wavy outer wall of the cone neck; y is the output value of the response surface function.

[0077] Based on the least squares method, the 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 of the asymmetric distribution function (A, B, ε1, ε2), four response surface functions are obtained respectively. The undetermined coefficients of each response surface function (a0, a1...a 20 ) as shown in Table 4.

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

[0079]

[0080] In order to evaluate the accuracy of the four response surface functions obtained, this embodiment calculates the relative errors and normalized root mean square errors between the output y of the four response surface functions and the four undetermined coefficient values ​​(A, B, ε1, ε2) of the asymmetric distribution function obtained in step 2. The relative errors are shown in Fig.13 , Fig.14 , Fig.15 and Fig.16 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 indicator used to measure the performance of the 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 fit of the prediction model. Table 5 shows that the fit of the four response surface functions is good.

[0085] Step 4: Predict the sound absorption coefficient of the acoustic superstructure based on the proxy model.

[0086] After building the proxy model, by changing the values ​​of the five input structural parameters, the unknown coefficients A, B, ε1, and ε2 of the corresponding output asymmetric distribution function can be obtained, and then a curve can be drawn based on the asymmetric distribution function to predict the sound absorption coefficient.

[0087] For example, change the values ​​of the five structural parameters input to: n1 =7mm, d n2 =4mm, l n =30mm, b=4mm, δ=0.6mm, the predicted sound absorption coefficient is as follows Fig.17As shown, the horizontal axis is the sound wave frequency and the vertical axis is the sound absorption coefficient. The accuracy of the prediction result can be verified by simulation. If the simulation result is consistent with the prediction result, it means that the proxy 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 (calculation of a single acoustic superstructure) of the prediction method for the sound absorption coefficient of 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 part of the embodiments of the present application, rather than all of the embodiments. The detailed description of the embodiments of the present application is not intended to limit the scope of the present application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are 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; Fitting a sound absorption coefficient curve through an asymmetric distribution function based on the least square method according to the acquired sound absorption coefficient data, and determining the undetermined coefficients of the asymmetric distribution function according to the fitted sound absorption coefficient curve; 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 following asymmetric distribution function is used: Where A, B, ε1, and 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; and f(x) is the output value of the asymmetric distribution function.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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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