Reduced Order Modeling and Analysis of Airfoil Flutter Using Dynamics-Based Autoencoders

Nonlinear flutter analysis is essential for ensuring the safety and performance of modern aeroelastic systems. Performing nonlinear stability analysis, however, is a challenging task for aeroelastic systems when relying on traditional approaches. This paper introduces a data-driven approach for nonintrusive nonlinear reduced order modeling and flutter analysis in aeroelastic systems. The proposed approach integrates nonlinear stability analysis for dynamical systems theory with machine learning techniques, enabling nonlinear flutter analysis with a limited number of simulated time-domain trajectories. This data-driven method determines reduced order models of systems exhibiting flutter instabilities and the transformation to and from the state space and the reduced order coordinates. Numerical results are provided to demonstrate the performance of the proposed method for a typical nonlinear airfoil section exhibiting supercritical and subcritical flutter.