Data-driven prediction of unsteady pressure distributions based on deep learning

Abstract

In the present work, an efficient Reduced-Order Model is developed for the prediction of motion-induced unsteady pressure distributions. The model is trained on the basis of synthetic data generated by full-order Computational Fluid Dynamics (CFD) simulations. The nonlinear identification task is to predict a snapshot representing the pressure distribution for the current time step based on respective snapshots of previous time steps and applied excitation. Once a Reduced-Order Model is conditioned on training data, it can predict sequences of the pressure distribution in a recurrent manner based on the excitation signal. Hence, it is able to capture the motion-induced nonlinear unsteady aerodynamics for a given configuration at fixed free-stream conditions. In this way, computationally extensive CFD simulations can be substituted by the application of the more efficient Reduced-Order Model. The nonlinear behavior of the aerodynamic system is captured based on a deep convolutional neural network. The performance of the Reduced-Order Model is demonstrated based on the LANN (Lockheed-Georgia, Air Force Flight Dynamics Laboratory, NASA-Langley and NLR) wing performing high-amplitude pitching motion in transonic flow. The unsteady aerodynamics of the considered test case is dominated by nonlinear effects due to complex moving shock structures both on the upper and lower surface of the wing. The Reduced-Order Model yields a superior prediction accuracy at a speed-up of more than three orders of magnitude compared to the employed CFD method.