Bayesian design of broadband multilayered microperforated panel absorbers

Area of Science:

• Acoustics
• Materials Science
• Statistical Inference

Background:

• Nonfibrous, hygienic sound absorbers are crucial for specific noise control and architectural acoustics applications.
• Microperforated panel (MPP) sound absorbers offer a potential solution but often have limited absorption bandwidth.
• Multilayer MPP systems can enhance absorption bandwidth but increase design complexity.

Purpose of the Study:

• To develop an efficient design framework for multilayer microperforated panel (MPP) sound absorbers.
• To address the increased design complexity associated with multilayered MPP systems.
• To enable inverse design from target specifications to MPP layer parameters.

Main Methods:

• Application of a Bayesian inferential framework for the design of multilayer MPP absorbers.
• Implementation of a parsimonious structural configuration that penalizes over-layering.
• Utilizing two levels of model-based inference: model selection and parameter estimation.

Main Results:

• The Bayesian framework simplifies the design of multilayer MPP absorbers.
• The inverse design process effectively determines the number of MPP layers and their parameters.
• Experimental validation confirmed the effectiveness of the Bayesian design approach.

Conclusions:

• A Bayesian inferential framework provides a practical and efficient method for designing multilayer MPP sound absorbers.
• This approach successfully balances absorption bandwidth enhancement with design complexity.
• The validated Bayesian design meets practical acoustic targets for multilayered MPP absorbers.