  Computational Fluid Dynamics (CFD) has become an indispensable tool in the
optimization design, and evaluation of aircraft aerodynamics. However, solving
the Navier-Stokes (NS) equations is a time-consuming, memory demanding and
computationally expensive task. Artificial intelligence offers a promising
avenue for flow field solving. In this work, we propose a novel deep learning
framework for rapidly reconstructing airfoil flow fields. Channel attention and
spatial attention modules are utilized in the downsampling stage of the UNet to
enhance the feature learning capabilities of the deep learning model.
Additionally, integrating the predicted flow field values generated by the deep
learning model into the NS equation solver validates the credibility of the
flow field prediction results. The NACA series airfoils were used to validate
the prediction accuracy and generalization of the deep learning model. The
experimental results represent the deep learning model achieving flow field
prediction speeds three orders of magnitude faster than CFD solver.
Furthermore, the CFD solver integrated with deep learning model demonstrates a
threefold acceleration compared to CFD solver. By extensively mining historical
flow field data, an efficient solution is derived for the rapid simulation of
aircraft flow fields.
