Inverse estimation of the Dunn and Davern model coefficients for jute material using the particle swarm optimization method

Abstract

Today many different natural materials are being effectively used in the acoustics and noise control domain. In this study, the acoustical characterization of three different types of natural jute felt material is performed by an experimental method and by using the Dunn and Davern model, along with an inverse characterization method. There are many empirical models available in the literature which describes the acoustical behavior of specific material accurately, as they are specially developed for that material. In this study, the possibility of using only the air flow resistivity based Delany–Bazley model and the Dunn–Davern model for acoustical performance prediction of jute material is tested. However, these two models do not show good matching with the experimental data throughout the frequency range of interest. Particularly in the low frequency region, the level of mismatch between experimental and model data is high. Therefore the inverse prediction of the coefficients $c_1-c_8$ in Dunn–Davern model using the particle swarm optimization (PSO) method is conducted, and new coefficients for jute material are found. These new coefficients better predict the acoustical performance of jute felts and reduce the mismatch level in the low-frequency region.