Data-Driven Forecasting of Postflutter Responses of Geometrically Nonlinear Wings

What is it about?

Predicting the flutter boundary and post-flutter dynamics of complex nonlinear aeroelastic systems is very challenging. This is due to difficulties in obtaining accurate models and to the computational effort of nonlinear dynamic aeroelastic computations. This paper investigates a recent data-driven method as an efficient approach for analyzing the post-flutter responses of large-dimensional nonlinear aeroelastic systems. Additionally, the method is introduced as a tool for performing parametric studies on these systems that are of importance for design. Analyses are conducted on a geometrically nonlinear wing benchmark. Results from the proposed method show good agreement with time-marching solution in the prediction of the flutter points and of subcritical post-flutter dynamics for various design parameters.

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