An approximation model based on convolutional neural networks (CNNs) is
proposed for flow field predictions. The CNN is used to predict the velocity
and pressure field in unseen flow conditions and geometries given the pixelated
shape of the object. In particular, we consider Reynolds Averaged Navier-Stokes
(RANS) flow solutions over airfoil shapes. The CNN can automatically detect
essential features with minimal human supervision and shown to effectively
estimate the velocity and pressure field orders of magnitude faster than the
RANS solver, making it possible to study the impact of the airfoil shape and
operating conditions on the aerodynamic forces and the flow field in near-real
time. The use of specific convolution operations, parameter sharing, and
robustness to noise are shown to enhance the predictive capabilities of CNN. We
explore the network architecture and its effectiveness in predicting the flow
field for different airfoil shapes, angles of attack, and Reynolds numbers.

本文提出了一种基于卷积神经网络（CNN）的近似模型，用于流场预测，该模型可以有效地估算速度和压力场，实现了对空气动力学力和流场的研究。