On the application of artificial neural network for the development of a nonlinear aeroelastic model

Aeroelastic Computational Fluid Dynamics simulations have traditionally been associated to a high computational cost, making them prohibitive in a initial phase of the design. Analytic models, which may not be accurate for nonlinear aerodynamics, have traditionally been utilized in order to size those structures. Recently, some authors have proposed the use of artificial neural networks to reduce the error in the prediction of aerodynamic coefficients of bluff bodies, which have separated flow over a substantial part of its wetted surface. This article proposes a method based on neural networks for calculating the dynamic aerodynamic coefficients of a flat plate. The procedure, which is applied for different network typologies (feed-forward and long-short term memory neural networks), is, then, coupled with a structural solver in order to create an aeroelastic reduced order model. The results are compared with CFD aeroelastic simulations, showing a high reduction of computational cost (99%) without penalties in the accuracy. The instabilities are captured and the mean deformation, amplitude and frequency of the motion are predicted. In addition, the different neural network models are compared evidencing that for the aeroelastic calculation feed-forward networks are most efficient in terms of accuracy and computational cost.