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Aerodynamic performance evaluation is an important part of the aircraftaerodynamic design optimization process; however, traditional methods arecostly and time-consuming. Despite the fact that various machine learningmethods can achieve high accuracy, their application in engineering is stilldifficult due to their poor generalization performance and "black box" nature.In this paper, a knowledge-embedded meta learning model, which fully integratesdata with the theoretical knowledge of the lift curve, is developed to obtainthe lift coefficients of an arbitrary supercritical airfoil under various angleof attacks. In the proposed model, a primary network is responsible forrepresenting the relationship between the lift and angle of attack, while thegeometry information is encoded into a hyper network to predict the unknownparameters involved in the primary network. Specifically, three models withdifferent architectures are trained to provide various interpretations.Compared to the ordinary neural network, our proposed model can exhibit bettergeneralization capability with competitive prediction accuracy. Afterward,interpretable analysis is performed based on the Integrated Gradients andSaliency methods. Results show that the proposed model can tend to assess theinfluence of airfoil geometry to the physical characteristics. Furthermore, theexceptions and shortcomings caused by the proposed model are analysed anddiscussed in detail.

Knowledge-embedded meta-learning model for lift coefficient prediction of airfoils