In this paper, a traveling wave model is proposed to explore its influence on the aerodynamic drag of a Ahmed model, the experimental and numerical results of aerodynamic drag coefficient C-D for the Ahmed model are in good agreement. Then by defining the aerodynamic benefit coefficient Delta C-D as the evaluation index for the orthogonal experiment, range analysis is conducted to determine the influences of the amplitude A, wavelength lambda and frequency omega of the wave and the vehicle speed u on Delta C-D. After the analysis it can been found that lambda has the least importance among these parameters, hence A, omega and u are used to construct the 105 samples for training the BP neural network to predict Delta(CD), results show that Delta C-D obtained from the neural network is significantly affected by the parameters of traveling wave. The prediction accuracy of the network is furtherly verified by another 15 samples which are also built on A, omega and u, and the corresponding data overlap rate of Delta C-D is 96 %, so it can be concluded that the BP neural network constructed in this paper is accurate enough to predict Delta C-D.
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