In 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.

In the present study, a nonlinear system identification approach based on a long short-term memory (LSTM) neural network is applied for the prediction of transonic buffet aerodynamics. The identification approach is applied as a reduced-order modeling (ROM) technique for an efficient computation of time-varying integral quantities such as aerodynamic force and moment coefficients. Therefore, the nonlinear identification procedure as well as the generalization of the ROM are presented. The training data set for the LSTM–ROM is provided by performing forced-motion unsteady Reynolds-averaged Navier–Stokes simulations. Subsequent to the training process, the ROM is applied for the computation of the aerodynamic integral quantities associated with transonic buffet. The performance of the trained ROM is demonstrated by computing the aerodynamic loads of the NACA0012 airfoil investigated at transonic freestream conditions. In contrast to previous studies considering only a pitching excitation, both the pitch and plunge degrees of freedom of the airfoil are individually and simultaneously excited by means of an user-defined training signal. Therefore, strong nonlinear effects are considered for the training of the ROM. By comparing the results with a full-order computational fluid dynamics solution, a good prediction capability of the presented ROM method is indicated. However, compared to the results of previous studies including only the airfoil pitching excitation, a slightly reduced prediction performance is shown.

In the present study, a deep learning approach based on a long short-term memory (LSTM) neural network is applied for the prediction of transonic wing buffet pressure. In particular, fluctuations in surface pressure over a certain time period as measured by a piezoresistive pressure sensor, are considered. As a test case, the generic XRF-1 aircraft configuration developed by Airbus is used. The XRF-1 configuration has been investigated at different transonic buffet conditions in the European Transonic Wind tunnel (ETW). During the ETW test campaign, sensor data has been obtained at different local span—and chordwise positions on the lower and upper surface of the wing and the horizontal tail plane. For the training of the neural network, a buffet flow condition with a fixed angle of attack $$\alpha$$α and a fixed sensor position on the upper wing surface is considered. Subsequent, the trained network is applied towards different angles of attack and sensor positions considering the flow condition applied for training the network. As a final step, the trained LSTM neural network is used for the prediction of pressure data at a flow condition different from the flow condition considered for training. By comparing the results of the wind tunnel experiment with the results obtained by the neural network, a good agreement is indicated.

In the present work, a reduced-order modeling (ROM) framework based on a recurrent neuro-fuzzy model (NFM) that is serial connected with a multilayer perceptron (MLP) neural network is applied for the computation of transonic aileron buzz. The training data set for the specified ROM is obtained by performing forced-motion unsteady Reynolds-averaged Navier Stokes (URANS) simulations. Further, a Monte Carlo-based training procedure is applied in order to estimate statistical errors. In order to demonstrate the method’s fidelity, a two-dimensional aeroelastic model based on the NACA65₁213 airfoil is investigated at different flow conditions, while the aileron deflection and the hinge moment are considered in particular. The aileron is integrated in the wing section without a gap and is modeled as rigid. The dynamic equations of the rigid aileron rotation are coupled with the URANS-based flow model. For ROM training purposes, the aileron is excited via a forced motion and the respective aerodynamic and aeroelastic response is computed using a computational fluid dynamics (CFD) solver. A comparison with the high-fidelity reference CFD solutions shows that the essential characteristics of the nonlinear buzz phenomenon are captured by the selected ROM method.