Equivalent Model Of Aluminum Foam Composite Structure And Its Sound Absorption Performance Study

Since porous materials have been used as sound-absorbing materials to reduce the impact of noise pollution on people’s lives,work,and studies,the question of how to design porous sound-absorbing materials with better sound absorption performance has become a major concern.Simulation technology can theoretically determine the structural parameters of porous sound-absorbing materials with optimal performance,effectively providing a theoretical foundation for designing porous sound-absorbing materials with excellent performance while reducing unnecessary experimental processes.In this study,a double-layer open-cell aluminum foam composite structure is designed.In order to more conveniently obtain the structural parameters of the composite structure with excellent sound absorption performance,three simulation prediction methods are proposed to simulate and predict the sound absorption coefficient of the open-cell aluminum foam composite structure.Which are nonlinear neural network simulation,analytic formula is constructed by dimensional analysis and finite element model simulation.The specific implementation process of the three simulation prediction methods and the simulation mock-up structure are introduced in the paper,respectively.The simulation prediction results of each method are also summarized and analyzed to provide a reference for the prediction of sound absorption coefficients of porous materials.In this study,two nonlinear neural networks are discussed to predict the sound absorption coefficient of open-cell aluminum foam composite structure.In terms of the prediction process and results,RBF neural network requires a small number of samples,only 11 groups of data for network training,and the prediction accuracy is also high,the error is less than 6%.EO optimization algorithm can theoretically solve the problem of large number of samples required by GRNN neural network.The specific approach is to use the optimization algorithm to process a small number of sample sets to obtain the best smooth factor value before establishing the GRNN neural network model,and then substitute it into the GRNN neural network model for simulation prediction.Although the process of EO-GRNN neural network is complex,the prediction accuracy is high,the prediction error is below 4%,and the sample size is small,only 11 groups of data are needed for network training.The mathematical analytical formula between the sound absorption coefficient of open-cell aluminum foam composite structure and its structural parameters was established by the method of dimensional analysis.After verification,it was found that the analytical formula has high accuracy,the average calculation error is only 0.23%,and the influence capacity of each structural parameter can be intuitively understood by observing the power index of each structural parameter in the analytical formula.Built two-and three-dimensional(2-and 3-D)finite element models simulation absorption coefficient of open-cell aluminum foam composite structure.Although the modeling process is complicated,the 3-D model can accurate to obtain the sound absorption coefficient of open-cell aluminum foam composite structure simulation values more accurately than the 2-D model,the simulation errors are about 10%,and the variation trend of the simulation values is consistent with the actual values,and it can provide the theoretical basis for subsequent production and preparation with the 3-D modeling data of the material.In this study,the classification of sound-absorbing materials and their principles,as well as the concrete implementation process and simulation structure of three kinds of simulation prediction methods are briefly introduced.The advantages and disadvantages of each method are summarized,which can provide reference for the prediction of sound absorption coefficient of open-cell aluminum foam composite structure by simulation technology.