Wind noise is a major contributor to vehicle noise and a very common consumer complaint for overall vehicle quality [1]. The reduction of wind noise is becoming even more important as powertrain noise is reduced or eliminated (by conversion to hybrid and electric vehicles) and as the importance of quiet interior environment for hands-free device use and voice activation systems becomes more pronounced. In contrast to other noise sources such as tires, engine, intake, exhaust or other component noise whose acoustic loads may be measured in a direct and standardized way with the proper equipment, wind noise is very difficult to predict because the acoustic part of wind noise is a small component of overall fluctuating pressures. It is very challenging to either directly measure or to simulate the acoustic component of fluctuating exterior pressures using CFD (Computational Fluid Dynamics) without a great deal of specialized experience in this area. This paper addresses the challenges of Aero-Vibro-Acoustics (AVA) modeling specifically for vehicle wind noise applications and describes what types of CFD analyses are suitable for driving vibro-acoustic models to predict wind noise and what methods for converting wind tunnel test data or CFD outputs to acoustic input loads are most effective. The various vibro-acoustic modeling techniques that have been successfully employed to predict and optimize interior noise via sensitivity studies of interior sound package and glass damping treatments are described. Validation examples and comparisons are shown and conclusions about current best modeling practices and future areas of investigation are presented:


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    Title :

    Aero-Vibro-Acoustic Simulation Methodologies for Vehicle Wind Noise Reduction


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    Symposium on International Automotive Technology 2019 ; 2019



    Publication date :

    2019-01-09




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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