NAFEMS Americas and Digital Engineering (DE) teamed up (once again) to present CAASE, the (now Virtual) Conference on Advancing Analysis & Simulation in Engineering, on June 16-18, 2020!

CAASE20 brought together the leading visionaries, developers, and practitioners of CAE-related technologies in an open forum, unlike any other, to share experiences, discuss relevant trends, discover common themes, and explore future issues, including:
-What is the future for engineering analysis and simulation?
-Where will it lead us in the next decade?
-How can designers and engineers realize its full potential?
What are the business, technological, and human enablers that will take past successful developments to new levels in the next ten years?

Resource Abstract

The evaluation and optimization of acoustical performance of poroelastic noise control treatments require a knowledge of the macroscopic material properties in additional to geometric properties. Unfortunately, the direct measurement of macroscopic material properties not only requires extensive laboratory equipment, but also prone to errors. On the other hand, measurement of the acoustical performance of noise control materials are quite straightforward. Consequently, inverse methods have been developed to estimate these properties from acoustic performance metrics such as sound transmission loss and absorption coefficient. Most of the prevailing inverse techniques use normal incidence sound transmission loss and/or absorption coefficient measured by impedance tubes applicable to single layer noise control treatments. While the estimation of fluid properties of these materials has been extensively investigated, the estimation of mechanical properties is generally not accurately predicted. This is circumvented by considering the elastic effect of the edge-constraint together with the finite element method modeling of impedance tube. These methods have been incorporated and are available in commercial elastic porous material characterization software.

Nevertheless, these methods still have limitations. First, most of the approaches have been based on normal incidence performance metrics measured using standing wave impedance tubes. However, to evaluate the effectiveness of the noise control treatments, automotive OEMs set targets based on random incidence performance metrics such as random incidence sound transmission loss. Second, these approaches are based on single layer tested in the impedance tubes. On the other hand, to quantify the effectiveness of the noise control treatments, sound transmission loss measurement from two-room suite under random incidence is widely used. The primary objective of this paper is to investigate the inverse characterization of macroscopic properties for of multi-layer noise control treatment using random incidence sound transmission loss measurements. To achieve this goal, a new method for material characterization of noise control treatment has been proposed. The proposed approach utilizes genetic algorithm where the fitness function is the difference between the test and simulations of insertion loss that is derived from sound transmission loss. The simulation results of sound transmission depend on the values of the material parameters, which are set to be the design variables. In each iteration during the optimization process, the sound transmission could be simulated from the updated values of the material properties of the noise control treatment. The material parameters are assumed to be successfully estimated when the fitness level is reached. The proposed approach is demonstrated by applying the method for the estimation of typical noise control treatments. Additionally, the robustness of the proposed approach is also investigated.