Estimation of Poroelastic Material Properties of Noise Control Treatments Using Model Order Reduction

Noise reduction is generally accomplished by applying appropriate noise control treatments at strategic locations. Noise control treatments consisting of poroelastic materials in layers are extensively used in noise control products. Sound propagation through poroelastic materials is governed by macroscopic material and geometric properties. Thus, a knowledge of material properties is important to improve the acoustical performance of the resulting noise control products. Since the direct measurement of these properties is cumbersome, these have been usually estimated indirectly from easily measurable acoustic performance metrics such as normal incidence sound transmission and/or absorption coefficient, measured using readily available impedance tube. The existing inverse characterization approaches fulfilled the estimation by curve fitting measured and predicted acoustic models. In this paper, in addition to the use of diffuse field performance metrics, a data driven machine learning approach is utilized to derive reduced order models. The properties of porous material are efficiently estimated by combining genetic algorithm with reduced order models. This approach is especially attractive to material suppliers, who usually lack information about material properties to improve their products.