Sound absorption prediction of high-density foam-formed softwood fibers

Abstract

Nowadays, porous absorption materials need to be environmentally sustainable. This concerns the sources of raw materials, low energy consumption in the manufacturing process, and options for recycling. Therefore, alternatives for mineral wools are needed and biomaterials offer an attractive solution. Some recently studied sustainable acoustic materials, such as foam-formed wood fibers, can be produced with a negative carbon footprint. However, sound absorption performance must match the less sustainable variants to ensure competitiveness. Therefore, the prediction of sound absorption is important because it supports the optimization of processes and functionality to achieve the best possible solution. In this study, foamformed wood pulps of increasing density are produced, characterized, and modeled by semi-phenomenological and analytical models. Their density range exceeds an earlier study by choosing a non-ionic surfactant during synthesis and model accuracy across densities is evaluated against experimentally measured sound absorption. The aim is to create foam-formed fibers with maximal density and the possibility to predict sound absorption with accurate models. The results show that the JCAL model performs better than the analytical models, but the less tedious analytical models provide good approximations. The finding contributes to the optimization of foamformed pulps by choosing feasible models for the aimed density.