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 (Formula presented.), reaching local maxima of about (Formula presented.), due to strong pressure gradients associated with pronounced shock oscillations.