Prediction of Flow Field Over Airfoils Based on Transformer Neural Network

Abstract

AbstractAirfoil flow field data acquisition is pivotal to the study of aerodynamics, traditionally relying on time-consuming computational fluid dynamics simulations or expensive wind tunnel tests. Herein, we introduce a new methodology leveraging Transformer Neural Network (TNN), which differs from conventional methodologies by employing self-attention mechanisms, to effectively predict these critical flow field data using historical data. A comprehensive set of experiments demonstrates the TNN model’s exceptional predictive accuracy, achieving over 95% across various airfoils under various operating conditions. Beyond accuracy and efficiency, we introduce an attention principle in our TNN model enhancing its interpretability. By aligning the TNN model’s attention distribution with the aerodynamic principles of airfoils, we illustrate how it utilises these geometric attributes in its predictions, thereby offering theoretical backing to its predictive outcomes. Our TNN model’s commendable accuracy, efficiency and interpretability illuminate the pathway for continued exploration in the fusion of deep learning with computational fluid dynamics.KEYWORDS: Deep learningTransformer Neural Networkairfoilflow field Disclosure statementNo potential conflict of interest was reported by the author(s).Data Availability StatementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Additional informationFundingThis work was supported by Scientific Research Project of Department of Education of Hunan Province [grant number 21C1577]; Natural Science Foundation of Hunan Province [grant number 2022JJ60090].