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Wind Noise Source Identification by Inverse Method in Wind Tunnel Test
ISSN: 0148-7191, e-ISSN: 2688-3627
Published June 05, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Wind noise in automobile is becoming more and more important as customer requirements increase. On the other hand great progress has been made on engine and road noises. Thus, for many vehicles, wind noise is the major acoustic source during road and motorway driving.
As for other noises, automobile manufacturers must be able for a new car project to specify, calculate and measure each step of the acoustic cascading:
- Transfers, both solid and air borne
In the case of automotive wind noise, the excitation source is the dynamic pressure on the vehicle’s panels. This part of the cascading is the one influenced by the exterior design. Even if many others components (panels, seals, cabin trims) have a big influence, the exterior design is a major issue for the wind noise. The wind noise level in the cabin can sometimes change significantly with only a small modification of the exterior design.
This paper addresses the problem of measuring the wind noise excitation source, and especially the low wavenumber part of the pressure on a vehicle’s panel. First, it reminds the phenomena existing in a turbulent flow, their consequences on the dynamic pressure on panels and the transfer function due to the panel. Then it presents a new Force Analysis Method which is specific to panels: the Panel Inverse Method (PIM). This new inverse method uses the numerical error done by a finite difference scheme to extract the efficient part of the pressure for wind noise. The advantages, inconveniences, limits and some improvements of the method are presented. Finally, the method is validated by various tests, including wind tunnel measurements on Renault Clio 4 and Laguna 3.
CitationBaudet, G., "Wind Noise Source Identification by Inverse Method in Wind Tunnel Test," SAE Technical Paper 2017-01-1784, 2017, https://doi.org/10.4271/2017-01-1784.
Data Sets - Support Documents
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