### 利用试验设计法建立翼型气动特性的人工神经网络模型

1. 西安交通大学 能源与动力工程学院
• 收稿日期:2009-04-11 修回日期:2009-07-20 出版日期:2010-05-25 发布日期:2010-05-25
• 通讯作者: 张楚华

### Artificial Neural Network Model of Airfoil Aerodynamic Performance Using Design of Experiments

Ju Yaping; Zhang Chuhua

1. School of Energy and Power Engineering, Xi’an Jiaotong University
• Received:2009-04-11 Revised:2009-07-20 Online:2010-05-25 Published:2010-05-25
• Contact: Zhang Chuhua

Abstract: Back propagation (BP) neural networks are established to predict the aerodynamic performance of an airfoil. Three typical types of design of experiments (DOE) methods, i.e., the factorial, orthogonal and uniform DOE, are employed to sample the training airfoils. The emphasis is laid upon the effect of the three DOE methods on the prediction accuracy of BP neural networks. The established BP neutral networks are finally applied to the optimization design of airfoil FX 63-167.The results show that, the factorial, orthogonal and uniform DOE have test-sample-averaged errors of 0.002%, 0.029% and 0.023% respectively in the case of small numbers of factors and levels. In the case of large numbers of factors and levels, the factorial DOE is unaccep-table due to excessive training points, while the orthogonal and uniform DOE have errors of 0.42% and 0.15%, respectively, indicating that the uniform DOE is the best method among the three, which can be coupled with the BP neural network for the prediction of the airfoil aerodynamic performance. The optimized airfoil shows a higher lift-drag ratio than the original one within the wide range of angles of attack from 0° to 18°. The lift-drag ratio increases by 4.38%, 1.38% and 5.51% respectively at angles of attack of 1°, 4° and 15°. The proposed method and conclusion can be extended to the multi-parameter optimization design of the turbo-machinery.