Bayesian design of broadband multilayered microperforated panel absorbers.

Abstract

In some noise control and architectural acoustics applications, nonfibrous, hygienic materials are desirable or even strictly required. In meeting such restrictive requirements, microperforated panel (MPP) sound absorbers represent a potential solution. Yet, they typically possess limited absorption bandwidth. Combining multiple MPPs into a multilayer system may broaden the absorption frequency ranges while maintaining high absorption. When increasing the overall absorption bandwidth, each additional MPP layer also increases the complexity of the design process because the design parameters are correspondingly increased by four per each additional layer. This paper applies a Bayesian inferential framework to the design of multilayer MPP absorbers with a parsimonious structural configuration, which penalizes the overlayered configurations. This Bayesian framework demonstrates that the practical design of multilayer MPP absorbers may be accomplished with two levels of model-based inference: model selection and parameter estimation. The design process proceeds inversely from a design target to design parameters, including the required number of MPP layers and their corresponding MPP parameters. This paper discusses the Bayesian design formulation, unified implementation of two levels of Bayesian inference, and experimental validation of a Bayesian design for a multilayered MPP absorber, which is able to meet the design target arising from practice.