Design and validation of microperforated panel absorbers using Occam's razor and causal inference

Abstract

Microperforated panel absorbers in forms of micro-poles /-slits (MPP/MSP) can achieve high absorption. However, noise control practice usually requires broadband high absorption. Single- or double-layer MSP/MPP absorbers often cannot meet practical requirements. Multilayers become a natural option. Multiple layers of MSP/MPP absorbers inherently complicates the design process upon a given design scheme. This work applies a Bayesian framework using a potentially multilayered prediction model. The Bayesian design involves two-levels of probabilistic inference to design a parsimonious number of layers using the model-selection solution, a quantitative implementation of Occam's razor, while the parameter estimation is used to estimate MPP/MSP parameters given the selected number of multilayers. This probabilistic design process rapidly hones in on the MPP/MSP parameters of each individual layer so that the overall composite meets the design goal. When experimentally validating the designed prediction upon the design scheme, manufacture inaccuracies may lead to deviations. In analyzing reasons of the deviations, this work further applies causal inference to analyze the cause-effect relationship. To this end a causal model has also been established. Using both parametric prediction model for the absorption performance and the causal model for causal inference of the deviation causes, the Bayesian multilayer design can be satisfactorily validated.