Study on Response Surface Methodology with Artificial Neural Networks Application in Aerodynamic Optimization

Author

Zheng, Wang ; Hu, Wu ; Hai-jun, Jia ; Ya-feng, Shi

Author_Institution

Dept. of Aeroengines, North western Polytech. Univ., Xian, China

Volume

5

fYear

2009

fDate

March 31 2009-April 2 2009

Firstpage

620

Lastpage

626

Abstract

Response surface methodology is currently commonly used and solutes to engineering design aerodynamic optimization problem. Artificial neural networks is the familiar approximation methods. It is trained and tested using a relatively small number of high fidelity CFD flow simulations. The ANN approximation was found to save on the simulation computation time and improved the generalization capability of the ANN model by reducing the overtraining or memorization problem.

Keywords

aerodynamics; aerospace engineering; computational fluid dynamics; design engineering; flow simulation; neural nets; optimisation; response surface methodology; ANN approximation; CFD flow simulations; artificial neural networks; engineering design aerodynamic optimization; generalization capability; memorization problem; response surface methodology; simulation computation time; Aerodynamics; Approximation methods; Artificial neural networks; Computational fluid dynamics; Computational modeling; Design engineering; Design optimization; Optimization methods; Response surface methodology; Testing; Artificial Neural Networks; Response Surface Methodology; aerodynamic optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Information Engineering, 2009 WRI World Congress on

Conference_Location

Los Angeles, CA

Print_ISBN

978-0-7695-3507-4

Type

conf

DOI

10.1109/CSIE.2009.458

Filename

5170609