Urban Fasel

Lecturer at Imperial College London

Data-driven aeroelastic reduced-order modeling

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 modelling 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 modelling 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 system, although the methods are generally applicable to any highly coupled aeroelastic system or dynamical system operating over multiple operating regimes. Our proposed modelling 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.


Data-driven nonlinear aeroelastic models of morphing wings for control

Nicola Fonzi, Steven L. Brunton, Urban Fasel

Proceedings of The Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 476(2239), 2020 Jun 28

A Balanced Mode Decomposition Approach for Equation-Free Reduced-Order Modeling of LPV Aeroservoelastic Systems

Andrea Iannelli, Urban Fasel, Nivethan Yogarajah, Roy S. Smith

AIAA Scitech 2021 Forum, 2021 10

The balanced mode decomposition algorithm for data-driven LPV low-order models of aeroservoelastic systems

Andrea Iannelli, Urban Fasel, Roy S. Smith

Aerospace Science and Technology, vol. 115, 2021 Jun 31