The Identification of Minimum Weight Sound Packages that Meet Specified Vehicle Interior Sound Pressure Levels 2019-01-1504
A vehicle’s fuel mileage is directly related to its CO2 emissions, which have a negative impact on the environment. This negative vehicle attribute can, of course, be mitigated by increasing the vehicle’s fuel mileage beyond current levels: the reduction of vehicle weight is one of the options automobile manufacturers can employ to meet that goal. Similarly, an electric vehicles range can be increased by reducing the vehicle’s weight. Therefore, the minimization of the weight of vehicle sound packages while maintaining their acoustical performance has a positive impact on the environment as well as on vehicle efficiency. In this research, a simple model of a vehicle front-of-dash sound package which consists of a limp porous layer placed in series with a flexible microperforated panel is considered. By varying the surface density and flow resistance of these two components, the sound absorption and transmission performance of the sound package can be balanced to achieve targeted interior sound levels. Previously, an analytical, transfer matrix approach to modeling both components of the sound package by using equivalent fluid models was combined with a Genetic Algorithm-based weight optimization process to identify minimum-weight sound packages that yield a specified interior level. That process has been extended in the present work by testing those sound package solutions in a more realistic context. In particular, a two-dimensional finite element analysis was performed to calculate the space-averaged pressure in an interior space having the cross-sectional shape of a generic vehicle. That is the previously-identified front-of-dash material combinations were combined with the geometry and sound absorption properties of a generic vehicle cabin. These calculations have shown that the optimum solutions identified by the analytical optimization process also result in near-optimal solutions in realistic geometries.
Hyunjun Shin, J. Stuart Bolton
Purdue Univ-West Lafayette
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