A CFD Analysis Method for Prediction of Vehicle Exterior Wind Noise

Paper #:
  • 2017-01-1539

Published:
  • 2017-03-28
DOI:
  • 10.4271/2017-01-1539
Citation:
Phan, V., Tanaka, H., Nagatani, T., Wakamatsu, M. et al., "A CFD Analysis Method for Prediction of Vehicle Exterior Wind Noise," SAE Int. J. Passeng. Cars - Mech. Syst. 10(1):286-298, 2017, doi:10.4271/2017-01-1539.
Pages:
13
Abstract:
High frequency wind noise caused by turbulent flow around the front pillars of a vehicle is an important factor for customer perception of ride comfort. In order to reduce undesirable interior wind noise during vehicle development process, a calculation and visualization method for exterior wind noise with an acceptable computational cost and adequate accuracy is required. In this paper an index for prediction of the strength of exterior wind noise, referred to as Exterior Noise Power (ENP), is developed based on an assumption that the acoustic power of exterior wind noise can be approximated by the far field acoustic power radiated from vehicle surface. Using the well-known Curle’s equation, ENP can be represented as a surface integral of an acoustic intensity distribution, referred to as Exterior Noise Power Distribution (ENPD). ENPD is estimated from turbulent surface pressure fluctuation and mean convective velocity in the vicinity of the vehicle surface. Therefore calculations and visualizations of ENP and ENPD can be implemented in an unsteady Computation Fluid Dynamics (CFD) simulation without extra cost for acoustical computation. Applications of ENP and ENPD in vehicle development process can provide not only quantitative information about the acoustic power of exterior wind noise in parts of vehicle but also an indication of the location of dominant sources of exterior wind noise as well as the associated turbulent flows. The information of ENP and ENPD are helpful in order to set targets for exterior wind noise around each part of vehicle and to establish countermeasure designs for reduction of exterior wind noise.
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