An optimization method for the design of acoustic metamaterial structures based on co-simulation

Abstract

The present invention discloses an optimization method for the structural design of acoustic metamaterials based on co-simulation, comprising the following steps: S101, parametrically modeling the acoustic metamaterials using COMSOL, and simultaneously adding data information such as multi-physical fields, boundary conditions, and material parameters to solve for the acoustic performance of the acoustic metamaterials; S102, programming in MATLAB to read the model parameters of COMSOL; S103, adding the SIMCODE component integrated with the MATLAB batch processing program to ISIGHT; S104, according to the performance curve diagram in step one, importing the finite element calculation data into the DATEMATCHING component and adding it to ISIGHT to perform a comparative analysis on the updated calculated model data. S105, adding the OPTIMATION optimization module component integrated with the multi-island genetic multi-objective optimization algorithm to ISIGHT. The present invention avoids complex algorithm programming, can improve the optimization efficiency and accuracy, and save time costs when meeting the design requirements of acoustic metamaterials.

Description

Technical Field

[0001] The present invention relates to the technical field of acoustic metamaterial structures, and specifically to an optimization method for the design of acoustic metamaterial structures based on co-simulation. Background Art

[0002] Acoustic metamaterials are one of the most cutting-edge disciplines in the field of acoustic research. They can effectively control sound waves by designing appropriate structural configurations, and have extraordinary acoustic properties, especially for the low-frequency (1 - 1000 Hz) noise problems that are difficult to solve with conventional sound insulation materials. The acoustic performance of acoustic metamaterials is based on the analysis of structural band gaps. The low-frequency band gap can suppress low-frequency noise, and the lower the band gap, the greater the optimization difficulty and the higher the requirements for the structure and materials. Therefore, how to design the structure of acoustic metamaterials and optimize the structure while ensuring the reasonable configuration of the metamaterials to improve their acoustic performance is an important research content.

[0003] Currently, many researchers adjust the structure of acoustic metamaterials manually to improve their acoustic performance. For the optimization design of multi-parameter acoustic metamaterial structures, this is cumbersome and cannot guarantee the optimization of the results. In addition, the programming design of the optimization algorithm and its association with finite element software are also relatively complex.

[0004] Therefore, there is currently a lack of an automatic optimization solution method for the design of acoustic metamaterial structures that can combine the optimization module of ISIGHT, based on the computational analysis of COMSOL, introduce the multi-island genetic algorithm, so that it can meet different optimization requirements of the metamaterials while meeting the structural requirements, improve the optimization efficiency and accuracy, and save time costs. Summary of the Invention

[0005] The purpose of the present invention is to provide an optimization method for the design of acoustic metamaterial structures based on co-simulation to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: An optimization method for the design of acoustic metamaterial structures based on co-simulation, including the following steps:

[0007] S101, use COMSOL to perform parametric modeling on the acoustic metamaterial, and at the same time add data information such as multi-physical fields, boundary conditions, and material parameters to solve the acoustic performance of the acoustic metamaterial;

[0008] S102, read the model parameters of COMSOL in MATLAB programming;

[0009] S103, add the SIMCODE component integrated with the MATLAB batch program to ISIGHT;

[0010] S104. Import the finite element calculation data into the DATEMATCHING component according to the performance curve graph in Step 1, and add it to ISIGHT to perform comparison and analysis on the updated calculated model data;

[0011] S105. Add the OPTIMATION optimization module component integrated with the multi-island genetic multi-objective optimization algorithm to ISIGHT;

[0012] S106. Run the calculation to obtain the optimal solution and complete the parameter design of the structure when the performance of the acoustic metamaterial is optimal.

[0013] Preferably, in Step S102, read the parameter model of cosmsol based on MATLAB programming and control the operation of COMSOL to realize the reading and writing of parameter variables and the output of data. The specific operations are as follows:

[0014] Program in MATLAB to realize the automatic connection with the COMSOL server, obtain the operation code of COMSOL, realize the reading and writing of model parameters and the control of the operation, and export the finite element calculation data through the program for analysis.

[0015] Preferably, in Step S103, the automatic update of the model program and analysis in COMSOL includes:

[0016] Modify the model parameter variables based on the driver program, drive COMSOL to perform updated calculations, and automatically update and export the finite element calculation results.

[0017] Preferably, in Step S104, the specific operations for analyzing and comparing the data results using DATEMATCHING are as follows:

[0018] Based on the driver file, make a curve graph for the finite element calculation results, and determine the characterization objectives for the optimization of the acoustic metamaterial structure according to the curve.

[0019] Preferably, in Step s105, it specifically includes:

[0020] a. For different optimization objectives, they are respectively 1) Design the bandgap interval of the acoustic metamaterial 2) Reduce the bandgap of the acoustic metamaterial 3) Improve the overall noise reduction performance of the acoustic metamaterial. The above optimization objectives can be described as 3maximizeSTL

[0021] Where is the bandgap of the acoustic metamaterial, and STL (sound transmission loss) characterizes the noise reduction performance of the acoustic metamaterial.

[0022] b. To ensure the stability of the metamaterial structure, the constraint conditions are:

[0023]

[0024] c. Since the optimization is targeted at the curve, for Targets 1 and 2, according to the bandgap curve, the data of the highest point of the first energy band can be extracted. Set the optimization function to 1 in ISIGHT. For the STL curve, the optimization function can be set to 3, where f(s) is the area enclosed by the curve and the x-axis, and f(t) is the maximum value of the STL curve.

[0025] Preferably, in step S106, according to the ISIGHT optimization process diagram, judge the convergence of the iterative calculation results, determine the global optimal solution, and perform verification and analysis in COMSOL.

[0026] Preferably, in step S101, after solving, relevant data such as the structural bandgap and noise reduction performance of the metamaterial are obtained, a parametric model is obtained, the size variables for the optimization design are determined as the rectangular widths a and b of the connecting ring and the thickness c of the support column, and the parametric model is analyzed and calculated to obtain the structural bandgap and noise reduction performance curve graphs of the metamaterial.

[0027] Preferably, in step S102, after reading the model parameters, then based on MATLAB programming, read the parametric model of COMSOL, control the operation of COMSOL, control the operation and solution of COMSOL as well as data output, realize the reading and writing of parameter variables and data output, and obtain the MATLAB driver file at the same time.

[0028] Preferably, in step S103, after the data is added, integrate the MATLAB batch processing program in ISIGHT using the SIMCODE component to realize the integration of COMSOL, so as to drive COMSOL to automatically update the parametric model and automatically analyze the updated model.

[0029] Preferably, in step S105, then determine the range of size variables to be optimized, the population individuals, the number of islands and the number of evolution generations and other algorithm parameters and optimization objectives of the multi-island genetic algorithm, and optimize the structural parameters of the acoustic metamaterial based on the multi-island genetic algorithm to respectively achieve: 1. Design the bandgap interval of the acoustic metamaterial; 2. Reduce the bandgap of the acoustic metamaterial; 3. Improve the overall noise reduction performance of the acoustic metamaterial.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] 1. By combining the optimization module of ISIGHT, based on the computational analysis of COMSOL, and introducing the multi-island genetic algorithm, the present invention optimizes the multi-structural parameters of acoustic metamaterials to meet different optimization requirements of the metamaterials. Compared with the traditional method of researchers manually debugging the structure of acoustic metamaterials to improve their acoustic performance, the engineering quantity is greatly simplified and more optimized results can be obtained. At the same time, for the optimization design of the multi-parameter acoustic metamaterial structure, it can better adapt to such complex and cumbersome functions, thus facilitating popularization and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 FIG. is the overall flowchart of a method for optimizing the design of an acoustic metamaterial structure based on co-simulation according to the present invention;

[0033] Figure 2 FIG. is the optimization process diagram of multi-structural parameters of an acoustic metamaterial based on co-simulation in a method for optimizing the design of an acoustic metamaterial structure based on co-simulation according to the present invention;

[0034] Figure 3 FIG. is the comparison diagram of the optimized effects in a method for optimizing the design of an acoustic metamaterial structure based on co-simulation according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0036] Please refer to Figures 1-3 , the present invention provides a technical solution: a method for optimizing the design of an acoustic metamaterial structure based on co-simulation, including the following steps:

[0037] S101, using COMSOL to perform parametric modeling on the acoustic metamaterial, and at the same time adding data information such as multi-physical fields, boundary conditions, and material parameters to solve the acoustic performance of the acoustic metamaterial;

[0038] S102, reading the model parameters of COMSOL in MATLAB programming;

[0039] S103, adding the SIMCODE component integrated with the MATLAB batch processing program to ISIGHT;

[0040] S104. Import the finite element calculation data into the DATEMATCHING component according to the performance curve graph in Step 1, and add it to ISIGHT to perform comparison and analysis on the updated calculated model data;

[0041] S105. Add the OPTIMATION optimization module component integrated with the multi-island genetic multi-objective optimization algorithm to ISIGHT;

[0042] S106. Run the calculation to obtain the optimal solution and complete the parameter design of the structure when the performance of the acoustic metamaterial is optimal.

[0043] In the S102 step, based on MATLAB programming, read the parameter model of cosmsol, and control the operation of COMSOL to realize the reading and writing of parameter variables and the output of data. The specific operations are as follows:

[0044] Program in MATLAB to realize the automatic connection with the COMSOL server, obtain the operation code of COMSOL, realize the reading and writing of model parameters and the control of operation, and export the finite element calculation data through the program for analysis.

[0045] In the S103 step, the automatic update of the model program and analysis in COMSOL includes:

[0046] Modify the model parameter variables based on the driver program, drive COMSOL to perform updated calculations, and automatically update and export the finite element calculation results.

[0047] In the S104 step, the specific operations for using DATEMATCHING to analyze and compare the data results are as follows:

[0048] Based on the driver file, make a curve graph for the finite element calculation results, and determine the characterization objectives for the optimization of the acoustic metamaterial structure according to the curve.

[0049] In the step s105, it specifically includes:

[0050] a. For different optimization objectives, they are respectively 1) Design the bandgap interval of the acoustic metamaterial 2) Reduce the bandgap of the acoustic metamaterial 3) Improve the overall noise reduction performance of the acoustic metamaterial. The above optimization objectives can be described as 3maximizeSTL

[0051] Where is the bandgap of the acoustic metamaterial, and STL (sound transmission loss) characterizes the noise reduction performance of the acoustic metamaterial.

[0052] b. To ensure the stability of the metamaterial structure, the constraint conditions are:

[0053]

[0054] c. Since the optimization is targeted at curves, for Target 1 and 2, according to the bandgap curve, the highest point data of the first energy band can be extracted. Set the optimization function to 1 in ISIGHT. For the STL curve, the optimization function can be set to 3, where f(s) is the area enclosed by the curve and the x-axis, and f(t) is the maximum value of the STL curve.

[0055] In the step S106, according to the ISIGHT optimization process diagram, judge the convergence of the iterative calculation results, determine the global optimal solution, and perform verification and analysis in COMSOL.

[0056] In the step S101, after solving, relevant data such as the structural bandgap and noise reduction performance of the metamaterial are obtained, a parametric model is obtained, the size variables for the optimization design are determined as the rectangular widths a, b of the connecting ring and the thickness c of the support column, and the parametric model is analyzed and calculated to obtain the structural bandgap and noise reduction performance curve diagrams of the metamaterial.

[0057] In the step S102, after reading the model parameters, then based on MATLAB programming, read the parametric model of COMSOL, control the operation of COMSOL, control the operation and solution of COMSOL as well as data output, realize the reading and writing of parameter variables and data output, and at the same time obtain the MATLAB driver file.

[0058] In the step S103, after the data addition is completed, integrate the MATLAB batch program in ISIGHT with the SIMCODE component to realize the integration of COMSOL, so as to drive COMSOL to automatically update the parametric model and automatically analyze the updated model.

[0059] In the step S105, then determine the range of size variables to be optimized, the population individuals, the number of islands and the number of evolutionary generations and other algorithm parameters and optimization objectives of the multi-island genetic algorithm, and optimize the structural parameters of the acoustic metamaterial based on the multi-island genetic algorithm, respectively realizing 1. the design of the bandgap interval of the acoustic metamaterial; 2. reducing the bandgap of the acoustic metamaterial; 3. improving the overall noise reduction performance of the acoustic metamaterial.

[0060] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variation thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0061] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the design of acoustic metamaterial structures based on co - simulation ， It includes the following steps: characterized in that: S101, Use COMSOL to perform parametric modeling on acoustic metamaterials, and at the same time add multi - physical field, boundary condition, and material parameter data information to solve the acoustic performance of acoustic metamaterials; S102, Read the model parameters of COMSOL through MATLAB programming; S103, Add the SIMCODE component integrated with the MATLAB batch program to ISIGHT; S104, According to the performance curve graph in step one, import the finite - element calculation data into the DATEMATCHING component and add it to ISIGHT to perform comparison and analysis on the updated calculated model data; S105, Add the OPTIMATION optimization module component integrated with the multi - island genetic multi - objective optimization algorithm to ISIGHT; S106, Run the calculation to obtain the optimal solution and complete the parameter design of the structure when the performance of the acoustic metamaterial is optimal.

2. A method for optimizing the design of acoustic metamaterial structures based on co - simulation according to claim 1, characterized in that: In step S102, based on MATLAB programming, read the parameter model of cosmsol and control the operation of COMSOL to achieve reading and writing of parameter variables and data output. The specific operations are as follows: Program in MATLAB to achieve automatic connection with the COMSOL server, obtain the running code of COMSOL, achieve reading and writing of model parameters and control of the operation, and export the finite - element calculation data through the program for analysis.

3. A method for optimizing the design of acoustic metamaterial structures based on co - simulation according to claim 1, characterized in that: In step S103, the automatic update of the model program and analysis in COMSOL includes: Modify the model parameter variables based on the driver program, drive COMSOL to perform updated calculations, and automatically update and export the finite - element calculation results.

4. A method for optimizing the design of acoustic metamaterial structures based on co - simulation according to claim 1, characterized in that: In step S104, the specific operations for using DATEMATCHING to analyze and compare the data results are as follows: Based on the driver file, make a curve graph for the finite - element calculation results and determine the characterization objectives for the optimization of the acoustic metamaterial structure according to the curve.

5. A method for optimizing the design of acoustic metamaterial structures based on co - simulation according to claim 1, characterized in that: In step s105, it specifically includes: a. For different optimization objectives, namely 1) designing the bandgap interval of acoustic metamaterials, 2) reducing the bandgap of acoustic metamaterials, and 3) enhancing the overall noise reduction performance of acoustic metamaterials, the above optimization objectives can be described as 3. Maximize STL, Among them is the acoustic metamaterial band gap, and STL (sound transmission loss) characterizes the noise reduction performance of the acoustic metamaterial; b. To ensure the stability of the metamaterial structure, the constraint conditions are: where a and b are the rectangular widths of the connecting rings, and c is the thickness of the support columns; c. Since the optimization targets the curve, for Targets 1 and 2, the highest point data of the first energy band can be extracted according to the bandgap curve. Set the optimization function to 1 in ISIGHT. = Target value 2. For the STL curve, the optimization function can be set to 3, where f(s) is the area enclosed by the curve and the x-axis, and f(t) is the maximum value of the STL curve.

6. A method for optimizing the design of acoustic metamaterial structures based on co - simulation according to claim 1, characterized in that: In step S106, according to the ISIGHT optimization process graph, judge the convergence of the iterative calculation results, determine the global optimal solution, and perform verification and analysis in COMSOL.

7. A method for optimizing the design of acoustic metamaterial structures based on co - simulation according to claim 1, It is characterized in that: In the step S101, after solution, the structural bandgap of the metamaterial and the data related to the noise reduction performance are obtained, a parametric model is obtained, the size variables for the optimization design are determined as the rectangular widths a and b of the connecting ring and the thickness c of the support column, and the parametric model is analyzed and calculated to obtain the structural bandgap curve and the noise reduction performance curve of the metamaterial.

8. A method for optimizing the design of an acoustic metamaterial structure based on co-simulation according to claim 1, It is characterized in that: In the step S102, after reading the model parameters, the parameter model of COMSOL is then read based on MATLAB programming, the operation of COMSOL is controlled, the operation solution and data output of COMSOL are controlled, the reading and writing of parameter variables and the output of data are realized, and at the same time a MATLAB driver file is obtained.

9. A method for optimizing the design of an acoustic metamaterial structure based on co-simulation according to claim 1, It is characterized in that: In the step S103, after the data addition is completed, the MATLAB batch processing program is integrated with the SIMCODE component in ISIGHT to realize the integration of COMSOL, so as to drive COMSOL to automatically update the parametric model and automatically analyze the updated model.

10. A method for optimizing the design of an acoustic metamaterial structure based on co-simulation according to claim 1, It is characterized in that: In the step S105, the range of the size variables to be optimized, the population individuals of the multi-island genetic algorithm, the algorithm parameters of the number of islands and the number of evolution generations, and the optimization objective are then determined. Based on the multi-island genetic algorithm, the structural parameters of the acoustic metamaterial are optimized, and respectively:

1. The design of the bandgap interval of the acoustic metamaterial is realized; 2. Reducing the bandgap of the acoustic metamaterial; 3. Improving the overall noise reduction performance of the acoustic metamaterial.

Images