Experimental validation of Bayesian design for broadband multilayered microperforated panel absorbers

Abstract

Single-layer microperforated panel (MPP) absorbers often exhibit limited absorbing bandwidth. A Bayesian inference framework (encompassing both model selection and parameter estimation) has been utilized to design broadband MPP absorbers. In this work, the broadband multilayered MPP absorbers are experimentally validated. In demonstrating ability to meet practical requirements on both high absorption and wide bandwidth, the current investigation uses a sample design scheme in the frequency range from 300 Hz to 2.4 kHz. A minimum requirement for three MPP layers and the relevant three-layer MPP parameters are derived by the two levels of Bayesian inference to meet the design scheme. MPP samples, based on the predicted design scheme, are fabricated and used to conduct normal-incidence sound absorption measurements in an impedance tube. In order to quantify the fabrication tolerance, the measured acoustic data are used to estimate the MPP parameters of the constructed absorber, using an inverse Bayesian inference method. This paper will discuss the initial MPP design, experimental validations of the designed absorption performance, as well as fabrication tolerance estimations of the MPP parameters.