Determination of acoustic parameters of porous sound insulation materials

In this study, characterization of the acoustic parameters of porous and fibrous materials used in passive noise control applications has been investigated. Such materials are frequently used in automotive, aerospace, medical, home appliance sectors and architectures where acoustical effects have critical consequences and acoustic comfort is important. Knowing the parameters affecting acoustic behavior is quite important for correct evaluation and usage of these kinds of materials. Also, acoustic performance of the porous materials can be analyzed with finite element and statistical energy based numeric analysis methods. Characteristic parameters of porous materials must be known in order to be able to perform acoustic analyzes. There are many direct measurement methods based on academic literature and specific standards to determine the acoustic parameters of porous materials. However, direct measurement methods are time consuming and quite expensive. Alternatively, these parameters can be obtained by an indirect study using phenomenological models that include acoustic parameters representing the propagation of the sound in the porous medium over a wide frequency range. This method requires the solution of an optimization problem which is created with characteristic curves which is obtained from impedance tube measurement and calculated from phenomenological models. It is necessary to validate the accuracy of the approaches and the method used to determine accurately the parameters affecting the acoustic behaviour of the materials. For this purpose, this study been carried out using a virtual impedance tube measurement to determinate and validate the estimation method. Full biot models of specimens have been created with elastic and acoustic parameters. Acoustic parameters were estimated from the obtained results by taking into account the energy losses in the porous material and equivalent acoustic modeling approaches. The accuracy of the predicted parameters was compared with reference to the acoustic parameters used in the virtual impedance tube measurement. In addition, the success of the optimization methods used in parameter estimation has also been examined. Python programming language and optimization libraries are used for required calculations and parameter estimations.