On-demand optimize design of sound-absorbing porous material based on multi-population genetic algorithm

Abstract

Porous material (PM) shows good sound absorption performance, however, the sound absorbing property of PM with different parameters are greatly different. In order to match the most suitable absorbing materials with the most satisfactory sound-absorbing performance according to the noise spectrum in different practical applications, multi-population genetic algorithm is used in this paper to optimize the parameters of porous sound absorbing structures that are commonly used according to the actual demand of noise reduction and experimental verification. The results shows that the optimization results of multi-population genetic algorithm are obviously better than the standard genetic algorithm in terms of sound absorption performance and sound absorption bandwidth. The average acoustic absorption coefficient of PM can reach above 0.6 in the range of medium frequency, and over 0.8 in the range of high frequency through optimization design. At a mid-to-high frequency environment, the PM has a better sound absorption effect and a wider frequency band than that of micro-perforated plate. However, it has a poor sound absorption effect at low frequency. So it is necessary to select suitable sound absorption material according to the actual noise spectrum.

Keywords

• multi-population genetic algorithm
• parameter optimize
• porous material