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| Automobile drag coefficient prediction combining sparse octree and convolutional neural network |
| Received:July 30, 2023 Revised:October 23, 2023 |
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| DOI：10.7511/jslx20230730001 |
| KeyWord:drag coefficient deep learning sparse octree convolutional neural network automobile |
| Author | Institution |
| 王刚 |
河北工业大学 机械工程学院, 天津 |
| 张瑞昊 |
河北工业大学 机械工程学院, 天津 ;中汽研天津汽车工程研究院有限公司, 天津 |
| 刘学龙 |
中汽研天津汽车工程研究院有限公司, 天津 |
| 袁海东 |
中汽研天津汽车工程研究院有限公司, 天津 |
| 韩旭 |
河北工业大学 机械工程学院, 天津 |
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| Abstract: |
| 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. |
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