Optimization of micro-perforated panel absorber backed with parallel-arranged cavities of different depths

Abstract

Micro-perforated panel absorber (MPA) is one of the most effective materials in noise control. For the MPA backed with parallel-arranged cavities of different depths (PCD-MPA), the panel thickness, perforation diameter, perforation rate, and cavity depth are essential parameters for absorption performance. In this study, a Genetic Algorithm (GA)-based optimization design method for PCD-MPA is proposed to achieve specific performance objectives. Firstly, a prediction model for absorption performance of PCD-MPA is proposed. Secondly, a GA optimization method suitable for PCD-MPA is established, taking into account interactive parameters and generating multi-objective and high-quality optimal solutions through iterative adjustments. Finally, an acoustic standard sample was fabricated for experimental validation and compared with analytical results. The findings demonstrate that the proposed optimization method can achieve nearly perfect absorption with a well-selected combination of parameters that meet specific absorption requirements.