Ulli, K. and Gade, U., "An Analysis on Automotive Side Window Buffeting Using Scale Adaptive Simulation," SAE Technical Paper 2017-01-1788, 2017.
Automotive window buffeting is a source of vehicle occupant’s discomfort and annoyance. Original equipment manufacturers (OEM) are using both experimental and numerical methods to address this issue. With major advances in computational power and numerical modelling, it is now possible to model complex aero acoustic problems using numerical tools like CFD. Although the direct turbulence model LES is preferred to simulate aero-acoustic problems, it is computationally expensive for many industrial applications. Hybrid turbulence models can be used to model aero acoustic problems for industrial applications. In this paper, the numerical modelling of side window buffeting in a generic passenger car is presented. The numerical modelling is performed with the hybrid turbulence model Scale Adaptive Simulation (SAS) using a commercial CFD code. While the acoustic generation is modelled by solving compressible Navier-Stokes equation, integral method Ffowcs-Williams & Hawkings (FWH) is used to model acoustic propagation in the computational domain. Certain investigation on the influence of rear view mirror (RVM) & divide pillar on buffeting noises are analyzed using a few flow and spectral techniques. Differences in the noise levels between front and rear window buffeting are also investigated. A 3D-cavity test model is considered to validate the modelling methodology. Investigations have shown the installation of a divide pillar on window have significantly minimized noise levels and appendages like the glass mounted RVM have a minimizing effect on the buffeting intensity. Also the noise levels of rear window buffeting are found to be higher than front window buffeting. Modelling window buffeting using a SAS model have shown to be a reliable and computationally less expensive option. The investigations using spectral technique like Fast Fourier Transform (FFT) band analysis gave a better insight in to buffeting problem.