  It's difficult to accurately predict the flow with shock waves over an
aircraft due to the flow's strongly nonlinear characteristics. In this study,
we propose an accuracy-enhanced flow prediction method that fuses deep learning
and reduced-order model to achieve fast flow field prediction for various
aerodynamic shapes. First, we establish the convolutional neural network-proper
orthogonal decomposition (CNN-POD) model for mapping geometries to the entire
flow field. Next, local flow regions containing nonlinear flow structures are
identified through POD reconstruction for enhanced modeling. Then, a new CNN
model is employed to map geometries to the local flow field. The proposed
method is finally applied in predicting transonic flow over airfoils. The
results indicate that the proposed enhanced DNN method can reduce the
prediction error of flow properties, particularly in the regions with shock
waves (up to 13%-46%). Additionally, the better efficiency and robustness of
the proposed methods have been validated in comparison to existing methods.
