In the aerodynamic design, optimization of the pressure distribution of airfoils is crucial for the aerodynamic components. Conventionally, the pressure distribution is solved by computational fluid dynamics, which is a time-consuming task. Surrogate modeling can leverage such expense to some extent, but it needs careful shape parameterization schemes for airfoils. As an alternative, deep learning approximates inputs-outputs mapping without solving the efficiency-expensive physical equations and avoids the limitations of particular parameterization methods. Therefore, this paper presents a data-driven approach for predicting the pressure distribution over airfoils based on Convolutional Neural Network (CNN). Given the airfoil geometry, a supervised learning problem is presented for predicting aerodynamic performance. Furthermore, we utilize a universal and flexible parametrization method called Signed Distance Function to improve the performances of CNN. Given the unseen airfoils from the validation dataset to the trained model, our model achieves predicting the pressure coefficient in seconds, with a less than 2% mean square error.

在空气动力学设计中，优化翼型压力分布对于空气动力学组件至关重要。通常，压力分布是通过计算流体动力学来解决的，这是一项耗时的任务。替代模型可以在某种程度上利用这种费用，但是需要对翼型进行仔细的形状参数化。作为替代方案，深度学习可以在不求解效率昂贵的物理方程的情况下近似输入-输出映射，并避免特定参数化方法的局限性。因此，本文提出了一种基于卷积神经网络（CNN）的数据驱动型翼型压力分布预测方法。给定机翼的几何形状，提出了一种监督学习问题，用于预测空气动力学性能。此外，我们利用一种称为签名距离函数的通用且灵活的参数化方法来改善CNN的性能。考虑到从验证数据集到训练模型之间看不见的机翼，我们的模型可以在几秒钟内预测压力系数，均方误差小于2％。