Koopman neural operator approach to fast flow prediction of airfoil transonic buffet

Abstract

Transonic buffet on airfoil is of great importance in the aerodynamic characteristics of aircraft. In the present work, a modified Koopman neural operator (KNO) is applied to predict flow fields during the transonic buffet process of the OAT15A [ONERA (National Office for Aerospace Studies and Research) Aerospatiale Transport aircraft 15 Airfoil] airfoil. Transonic buffet flow with different angles of attack is simulated by Reynolds averaged numerical simulation with the Menter's k−ω shear stress transport (SST) model at Reynolds number Re=3×106. A prediction model is directly constructed between the flow fields at several previous time nodes and that at the future time node by KNO. The predictions of flow fields with single sample and multi samples are performed to demonstrate the prediction accuracy and efficiency of KNO. The prediction of sequence flow fields based on the iterative prediction strategy is achieved for the transonic buffet process. The results indicate that KNO can achieve a fast and accurate prediction of flow physical quantities for the transonic buffet. Compared with other deep learning models including Unet and Fourier neural operator, KNO has a more advanced capability of predicting airfoil transonic buffet flow fields with higher accuracy and efficiency and less hardware requirements.