This work presents a novel automobile drag coefficient prediction method by combining sparse octree and convolutional neural network(CNN),aiming at the problem that it is difficult for parametric methods to accurately represent the vehicle exterior styling.Based on the octree discrete method,the vehicle exterior shape is first discretized and simplified by using the normal vectors.With the aid of CNN,the exterior styling features are then extracted,which improves the speed of prediction for automobile drag coefficient significantly.By changing the number of convolutional layers and fully connected layers,the influence of different convolutional neural network structures on the prediction accuracy of automobile drag coefficient is investigated in detail.Numerical studies demonstrate that the present method can give more accurate detailed descriptions for vehicle styling compared with the traditional parametric methods.Therefore,it effectively improves the prediction accuracy and calculation efficiency,as the minimum prediction error yielded by the present CNNs is 1.453％and the calculation speed is 1620 times higher than CFD simulation.
万方数据
维普
