Imaging based pore network modeling of acoustical materials

The acoustical behavior of porous materials is dictated by their underlying pore network geometry. Given the complexity of accurately characterizing the various pore network features, current acoustical models instead rely on indirectly incorporating these features by accounting for them within acoustical transport properties, such as tortuosity, viscous and thermal characteristic lengths, and flow resistivity. In turn, these transport properties are currently identified using inverse characterization techniques or using multiphysics modeling techniques. Here, we propose the use of advanced image processing methods to characterize the pore network of acoustical materials and allow the direct calculation of their transport and acoustical properties. To establish the feasibility of this idea, we create 3D printable CAD models of porous materials with controlled pore geometries and use a Matlab-based watershed segmentation technique to calculate their effective pore and throat size distributions. These distributions are then used to calculate their transport properties and predict their sound absorption coefficients using the Johnson–Champoux–Allard model. For comparison, we calculate the transport properties using the hybrid multiphysics modeling technique and the inverse characterization method. The predictions from the three different methods are then compared with experimental measurements obtained by printing and testing the models using an impedance tube.