Abstract
A model-based design approach for microperforated panel absorbers comprised of multiple panel layers is developed. Microperforated panels (MPPs) are becoming increasingly popular as sound absorbers, capable of providing broadband absorption with high absorption coefficients, without the use of traditional porous materials. To increase the bandwidth of the intrinsically peaked narrowband absorption of a single MPP, multiple such panels can be combined into composite sound absorbers. We propose a method based on Bayesian inference to design multilayered MPP absorbers capable of producing a user-specified absorption profile. Using nested sampling to accumulate Bayesian evidence and to implement Occam's razor, the method produces a design requiring the fewest number of MPP layers while meeting the specified design requirements.
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