Optimization design and analysis of honeycomb micro-perforated plate broadband sound absorber. (15th January 2022)

• Record Type: Journal Article
• Authors: Yan, Shanlin; Wu, Jinwu; Chen, Jie; Xiong, Yin; Mao, Qibo; Zhang, Xiang
• Is Part Of: Applied acoustics. Volume 186(2022)
• Publication Date: 2022-01-15
• Subjects: Micro-perforated plate -- Change rate of sound absorption coefficient -- Cavity depth -- Particle swarm optimization algorithm -- Broadband sound absorption; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2021.108487
• Languages: English
• ISSNs: 0003-682X

Abstract

Abstract: The sound absorption performance of micro-perforated plate (MPP) is mainly restricted by four parameters: cavity depth, aperture, plate thickness and perforation rate. The concept of change rate of sound absorption coefficient is introduced, which is taken as the measuring standard to judge the influence of changing these parameters on the sound absorption performance of MPP. Generally, the parameter sensitivity of MPP sound absorber from large to small is as follows: cavity depth, aperture, perforation rate, plate thickness. According to the characteristic that the resonant frequency of MPP will shift when the cavity depth changes, combined with the acoustoelectric analogy principle, two kinds of single-layer honeycomb micro-perforated plate (HMPP) structures with different cavity depth are designed by using particle swarm optimization algorithm. Simulation and experimental results show that the structures can use their own different depth of honeycomb cores to achieve the purpose of broadband sound absorption. The feasibility of using particle swarm optimization algorithm to design broadband sound absorber is verified. In addition, through the contrastive analysis of the two structures, compared to the four regions HMPP, the experimental results of the seven regions HMPP are closer to the theoretical and simulation results, which is consistent with the characteristics of particle swarm optimization (PSO): the more optimization parameters are, the better the performance is.