Aerodynamic Shape Optimization Based on the MIRA Reference Car Model Jilin Univ

Format:

Conference/Event

Author/Creator:

Zhang, Zhang, author.

Contributor:

Ding, Wei

Zhang, Yu

Conference Name:

SAE 2014 World Congress & Exhibition (2014-04-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2014

Summary:

AbstractAutomobile industry is facing the great challenge of energy conservation and emission reduction. It's necessary to do some researches on some surface components of a car body to find out which of them may affect aerodynamic drag remarkably. This will help an aerodynamic engineer modify an initial car model more clearly. We also hope to reduce the cost during the process, including time and resources. In this paper, with the purpose of developing an aerodynamic shape optimization process and realizing its automation, a MIRA reference car model was studied and three commercial softwares were integrated-Altair HyperStudy, HyperMesh and CD-adapco STAR-CCM+. The optimization strategy in this paper was: firstly, a DOE (design of experiment) matrix, which contained four design factors and thirty levels was created. The baseline model was morphed according to the DOE matrix. Then the morphed model's aerodynamic drag coefficient (Cd) and lift coefficient (Cl) were calculated via CFD software. Both of them were used as the response variables. Secondly, after finishing all the CFD simulations, an approximate model was established on the basis of the DOE matrix. Finally, an optimization algorithm was performed. As a result, an optimized combination of the four design variables was obtained and it met the demands: Cd was minimum and Cl was less than zero. The combination was tested via a true CFD simulation to ensure the result's accuracy. In the end, the drag coefficient decreased by 19.2%

Notes:

Vendor supplied data

Publisher Number:

2014-01-0603

Access Restriction:

Restricted for use by site license