Application of a long short-term memory neural network for modeling transonic buffet aerodynamics

In the present work, a reduced-order modeling (ROM) framework based on a long short-term memory (LSTM) neural network is applied for the prediction of transonic buffet aerodynamics. This type of network has a high potential for modeling sequential data, which is favorable for capturing the time-delayed effects associated with unsteady aerodynamics. Therefore, the nonlinear identification procedure as well as the generalization of the resulting ROM are presented. Further, a Monte-Carlo-based training procedure is performed in order to estimate statistical errors. The training data set for the ROM is provided by means of forced-motion unsteady Reynolds-averaged Navier Stokes (URANS) simulation. Subsequent to the training process, the ROM is applied for the computation of time-varying integral quantities such as aerodynamic force and moment coefficients. The most challenging aspect when considering buffet aerodynamics is given by the reproduction of the self-sustained unsteadiness of the buffeting flow. Even without any external excitation, the flow is characterized by large shock-boundary layer interaction, resulting in shock movement and flow separation. Finally, the performance of the trained network is demonstrated by predicting the aerodynamic loads of the NACA0012 airfoil considered at transonic freestream conditions. Therefore, the airfoil is excited by a forced pitching motion beyond the buffet-critical angle of attack. A comparison with a full-order computational fluid dynamics (CFD) solution shows that the essential characteristics of the nonlinear buffet phenomenon are captured by the ROM method.