Skip to main content

Multi-Parameter Optimization of Automotive Rear View Mirror Region for Reducing Aerodynamic Noise

Buy Article:

$17.00 + tax (Refund Policy)

The excessive aerodynamic noise is one of the main concerns to vehicle passengers. Reducing aerodynamic noise is important for the automotive industry. This study presented a multi-parameter optimization method to reduce the aerodynamic noise in the rear view mirror region of automobiles. The accuracy of the numerical prediction and the efficiency of the optimization were investigated. The numerical results were verified by the wind tunnel experiments. The parametric modeling, genetic algorithm and subdomain simulation method were used to increase the optimization efficiency. A parametric model was built up to reshape it with different parameters. Two samples were generated through a hybrid genetic algorithm to achieve a fast convergence. The evaluation of samples was conducted by the subdomain simulation method which kept the accuracy of computational aeroacoustic calculation and saved 96% of the computational time. Based on the optimization method, the noise reduction of 4.1 dB(A) in the sound power level was achieved. The mitigation of vortices behind the region of the rear view mirror and A-pillar plays a main role in the aerodynamic noise reduction. A whole domain simulation applied to the optimization scheme was performed for the validation of the subdomain simulation method. The overall sound pressure level was decreased 2.08 dB(A) in the area of the front seat cavity and 1.35 dB(A) in the region of the rear seat cavity.

The requested document is freely available to subscribers. Users without a subscription can purchase this article.

Sign in

Keywords: 13.2.1; 21.6

Document Type: Research Article

Affiliations: Shanghai Automotive Wind Tunnel Center, Tongji University & Shanghai Key Lab of Vehicle Aerodynamics and Vehicle Thermal Management Systems

Publication date: 01 January 2018

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content