Prediction of wing buffet pressure loads using a convolutional and recurrent neural network framework

Abstract

AbstractIn the present study, a hybrid deep learning reduced-order model (ROM) is applied for the prediction of wing buffet pressure distributions on a civil aircraft configuration. The hybrid model is compound of a convolutional variational neural network autoencoder (CNN-VAR-AE) and a long short-term memory (LSTM) neural network. The CNN-VAR-AE is used for the reduction of the high-dimensional flow field data, whereas the LSTM is applied to predict the temporal evolution of the pressure distributions. For training the neural network, experimental buffet data obtained by unsteady pressure sensitive paint measurement (iPSP), is applied. As a test case, the Airbus XRF-1 configuration is selected, considering two different experimental setups. The first setup is defined by a wind tunnel model with a clean wing, whereas the second setup includes an ultra high bypass ratio engine nacelle on each wing. Both configurations have been tested in the European Transonic Windtunnel, considering several transonic buffet conditions. Finalizing the training of the hybrid neural networks, the trained models are applied for the prediction of buffet flow conditions which are not included in the training data set. A comparison of the experimental results and the pressure distributions predicted by the hybrid ROMs indicate a precise prediction performance. Considering both aircraft configurations, the main buffet flow features are captured by the hybrid ROMs.