Hierarchical meta-porous materials as sound absorbers

TL;DR

This paper explores hierarchical designs of porous materials to enhance sound absorption, especially at low frequencies, by using multi-scale recursive patterns, resulting in improved performance and material efficiency.

Contribution

It introduces hierarchical multilayered porous media with periodic and gradient structures, demonstrating enhanced sound absorption and reduced material use through combined analytical and numerical methods.

Findings

Hierarchical structures improve sound absorption compared to non-hierarchical materials.

Multiple levels of hierarchy increase absorption and reduce porous material needed.

Analytical and numerical results show excellent agreement.

Abstract

The absorption of sound has great significance in many scientific and engineering applications, from room acoustics to noise mitigation. In this context, porous materials have emerged as a viable solution towards high absorption performance and lightweight designs. However, their performance is somehow limited in the low frequency regime. Inspired by the concept of recursive patterns over multiple length scales typical of many natural materials, here, we propose a hierarchical organization of multilayered porous media and investigate their performance in terms of sound absorption. Two types of designs are investigated: a hierarchical periodic and a hierarchical gradient. In both cases it is found that the introduction of multiple levels of hierarchy allows to simultaneously (i) increase the level of absorption compared to the corresponding bulk block of porous material, along with (ii) a reduction of the quantity of porous material required. Both the cases of normal and oblique incidences are examined. The methodological approach is based on the transfer matrix method, optimization algorithms (metaheuristic Greedy Randomized Adaptive Search Procedure), and finite element calculations. An excellent agreement is found between the analytical and the numerical simulations.