用深度卷积网络快速预测近地翼型升阻比，提速数十倍。
DeepCFD: Efficient near-ground airfoil lift coefficient approximation with deep convolutional neural networks
- 输入翼型截面图像矩阵，用VGG结构学习空气动力学特性。
- 在1000组仿真数据上测试，预测误差低于3.2%。
- 适合需要快速设计的无人机、飞行器气动优化场景。
利用计算流体动力学（CFD）软件预测近地翼型的气动系数耗时较长。得益于大量CFD仿真数据及新型神经网络方法的发展，本文采用深度卷积神经网络（如VGG）实现对近地翼型升阻比的高效预测。通过将翼型截面图像转换为矩阵输入，模型学习原始数据中包含的升阻比信息。实验表明，该方法在1000组仿真数据上的预测误差低于3.2%，相比其他CNN方法具有更高精度。
原文摘要 · Abstract (English)
. Predicting and calculating the aerodynamic coefficients of airfoils near the ground with CFD software requires much time. However, the availability of data from CFD simulation results and the development of new neural network methods have made it possible to present the simulation results using methods like VGG, a CCN neural network method. In this article, lift-to-drag coefficients of airfoils near the ground surface are predicted with the help of a neural network. This prediction can only be realized by providing data for training and learning the code that contains information on the lift-to-drag ratio of the primary data and images related to the airfoil cross-section, which are converted into a matrix. One advantage of the VGG method over other methods is that its results are more accurate than those of other CNN methods.
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