  Aerodynamic performance evaluation is an important part of the aircraft
aerodynamic design optimization process; however, traditional methods are
costly and time-consuming. Despite the fact that various machine learning
methods can achieve high accuracy, their application in engineering is still
difficult due to their poor generalization performance and "black box" nature.
In this paper, a knowledge-embedded meta learning model, which fully integrates
data with the theoretical knowledge of the lift curve, is developed to obtain
the lift coefficients of an arbitrary supercritical airfoil under various angle
of attacks. In the proposed model, a primary network is responsible for
representing the relationship between the lift and angle of attack, while the
geometry information is encoded into a hyper network to predict the unknown
parameters involved in the primary network. Specifically, three models with
different architectures are trained to provide various interpretations.
Compared to the ordinary neural network, our proposed model can exhibit better
generalization capability with competitive prediction accuracy. Afterward,
interpretable analysis is performed based on the Integrated Gradients and
Saliency methods. Results show that the proposed model can tend to assess the
influence of airfoil geometry to the physical characteristics. Furthermore, the
exceptions and shortcomings caused by the proposed model are analysed and
discussed in detail.
