A general deep transfer learning framework for predicting the flow field of airfoils with small data

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The flow field under different flow conditions contains abundant structure information and is of great significance for aerodynamic analysis and aircraft design. Deep learning (DL) techniques have received great interest for flow field prediction due to their high capability of capturing structure characteristics in recent years. However, successful training of DL models usually requires a large amount of data, whose acquisition is prohibitively expensive and learning from insufficient flow field data still remains underexplored for DL models. This paper proposes a general deep transfer learning framework to predict the flow field of airfoils with insufficient data by transferring knowledge learned from other conditions with a large amount of data. A novel and robust flow field prediction model based on the idea of generative adversarial networks, BiFlowAN, is first proposed to precisely predict the flow field over an airfoil under a condition with sufficient data. Transfer learning technique is then introduced to transfer the learned knowledge of BiFlowAN to a new model, BiFlowAN-TL, for improving the generalization of the model under another condition with a small-scale dataset. Compared with other alternatives, the flow field predictions of supercritical airfoils show that BiFlowAN achieves higher accuracy and generalization in predicting the flow fields with sufficient data and BiFlowAN-TL significantly improves the prediction accuracy than BiFlowAN on the small-scale dataset, exhibiting the potential applicability of the proposed methods to solve the problem of rapidly and accurately evaluating aerodynamic performance with insufficient data.