Vibro-Acoustic Design Optimization Study to Improve the Sound Pressure Level Inside the Passenger Cabin

The interior noise inside the passenger cabin of automobiles can be classified as structure-borne or airborne. In this study, we investigate the structure-borne noise, which is mainly caused by the vibrating panels enclosing the vehicle. Excitation coming from the engine causes the panels to vibrate at their resonance frequencies. These vibrating panels cause a change in the sound pressure level within the passenger cabin, and consequently generating an undesirable booming noise. It is critical to understand the dynamics of the vehicle, and more importantly, how it interacts with the air inside the cabin. Two methodologies were used by coupling them to predict the sound pressure level inside the passenger cabin of a commercial vehicle. The Finite Element Method (FEM) was used for the structural analysis of the vehicle, and the Boundary Element Method (BEM) was integrated with the results obtained from FEM for the acoustic analysis of the cabin. The adopted FEM-BEM approach can be utilized to predict the sound pressure level inside the passenger cabin, and also to determine the contribution of each radiating panel to the interior noise level. The design parameters of the most influential radiating panels (i.e., thickness) can then be optimized to reduce the interior noise based on the three performance metrics. A structured parametric study, based on techniques from the field of industrial design of experiments (DOE) was employed to understand the relationship between the design parameters and the performance metrics. A DOE study was performed for each metric to identify the components that have the highest contribution to the sound pressure levels inside the cabin. For each run, the vibro-acoustic analysis of the system is performed, the sound pressure levels are calculated as a function of engine speed and then the performance metrics are calculated. The highest contributors (design parameters) to each performance metric are identified and regression models are built to be used for optimization studies. Then, preliminary optimization runs are employed to improve the interior sound pressure levels by finding the optimum configurations for the panel thicknesses. Our results show that the methodology developed in this study can be effectively used for improving the design of the panels to reduce interior noise when the vibro-acoustic response is chosen as the performance criteria.