2 research outputs found
Data-driven nonlinear aeroelastic models of morphing wings for control
Accurate and efficient aeroelastic models are critically important for
enabling the optimization and control of highly flexible aerospace structures,
which are expected to become pervasive in future transportation and energy
systems. Advanced materials and morphing wing technologies are resulting in
next-generation aeroelastic systems that are characterized by highly-coupled
and nonlinear interactions between the aerodynamic and structural dynamics. In
this work, we leverage emerging data-driven modeling techniques to develop
highly accurate and tractable reduced-order aeroelastic models that are valid
over a wide range of operating conditions and are suitable for control. In
particular, we develop two extensions to the recent dynamic mode decomposition
with control (DMDc) algorithm to make it suitable for flexible aeroelastic
systems: 1) we introduce a formulation to handle algebraic equations, and 2) we
develop an interpolation scheme to smoothly connect several linear DMDc models
developed in different operating regimes. Thus, the innovation lies in
accurately modeling the nonlinearities of the coupled aerostructural dynamics
over multiple operating regimes, not restricting the validity of the model to a
narrow region around a linearization point. We demonstrate this approach on a
high-fidelity, three-dimensional numerical model of an airborne wind energy
(AWE) system, although the methods are generally applicable to any highly
coupled aeroelastic system or dynamical system operating over multiple
operating regimes. Our proposed modeling framework results in real-time
prediction of nonlinear unsteady aeroelastic responses of flexible aerospace
structures, and we demonstrate the enhanced model performance for model
predictive control. Thus, the proposed architecture may help enable the
widespread adoption of next-generation morphing wing technologies