Deep Learning Framework for Predicting Transonic Wing Buffet Loads Due to Structural Eigenmode-Based Deformations

In the present paper, a reduced-order modeling (ROM) approach based on a hybrid neural network is presented in order to calculate wing buffet pressure distributions due to structural eigenmode-based deformations. The accurate prediction of unsteady surface pressure distributions is crucial for assessing aeroelastic stability and preventing structural failure, but full-order simulations are computationally expensive; the proposed ROM provides a fast and efficient alternative with a sufficient level of accuracy. The hybrid ROM is defined by a series connection of a convolutional autoencoder (CNN-AE) and a long short-term memory (LSTM) neural network. As a test case, the NASA Common Research Model (CRM) configuration for the transonic buffet condition is applied. Forced-motion computational fluid dynamics (CFD) simulations are conducted in order to obtain the aerodynamic responses induced by the eigenmode-based deformations. For the unsteady simulations, the triangular adaptive upwind (TAU) solver of the German Aerospace Center (DLR), is used. Based on a selected structural model, symmetric and asymmetric eigenmode-based deformations of the wing structure are implemented and considered for performance evaluation. Comparing the pressure loads modeled by the hybrid ROM and the reference full-order numerical solution, an overall good prediction performance is indicated with mean squared error (MSE) values mostly below 3%, reaching local maxima of about 12%, due to strong pressure gradients associated with pronounced shock oscillations.