CFD/CAE Combinations in Open Cavity Noise Predictions for Real Vehicle Sunroof Buffeting

Event
SAE 2013 World Congress & Exhibition
Authors Abstract
Content
Though some practitioners consider the simulation process for sunroof and side window buffeting to be mature, there remain considerable uncertainties and inefficiencies as how in predictive methodologies to account for interior panel flexibility, vehicle structural stiffness, seals leakages and interior materials surface finish. Automotive OEMs and component suppliers rightly target flow simulation of open sunroofs and passenger windows with a view to reducing the severely uncomfortable low-frequency booming disturbance. The phenomenon is closely related to open cavity noise experienced also in other transportation sectors; for example in Aerospace, landing gear and store release cavities, and in Rail Transportation, cavities for HVAC intakes and the bogie environment.
Recent studies published by the author demonstrate that the uncertainties can be correctly quantified by modeling. This publication defines a hierarchy of CFD/CAE based methods which overcome many of the a-posteriori tuning of simulations based on experiment, and considerably improve the predictive nature and efficiency of the simulation process. The methods range from fully deterministic simulations to phenomenological models requiring standard experimental pre-qualifications of the acoustical response of the system. The former involves CAE-coupling of CFD (Computational Fluid Dynamics) to CAA (Computational Aeroacoustics) and to CSM (Computational Structural Mechanics). The latter incorporates new correlation models published here for the first time.
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DOI
https://doi.org/10.4271/2013-01-1012
Pages
9
Citation
Mendonca, F., "CFD/CAE Combinations in Open Cavity Noise Predictions for Real Vehicle Sunroof Buffeting," SAE Int. J. Passeng. Cars - Mech. Syst. 6(1):360-368, 2013, https://doi.org/10.4271/2013-01-1012.
Additional Details
Publisher
Published
Apr 8, 2013
Product Code
2013-01-1012
Content Type
Journal Article
Language
English