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Data-driven nonlinear aeroelastic models of morphing wings for control
N Fonzi1, S L Brunton2, U Fasel1
1CMASLab, ETH Zurich, 8092 Zurich, Switzerland.
Proceedings. Mathematical, Physical, and Engineering Sciences
|August 25, 2020
Summary
This study introduces advanced data-driven methods to create accurate aeroelastic models for flexible aircraft. These models capture complex nonlinear dynamics across various conditions, improving control and enabling next-generation aerospace designs.
Area of Science:
- Aerospace Engineering
- Computational Fluid Dynamics
- Control Systems
Background:
- Highly flexible aerospace structures require accurate aeroelastic models for optimization and control.
- Next-generation systems exhibit nonlinear, coupled aerodynamic and structural dynamics.
Purpose of the Study:
- Develop accurate, tractable, data-driven reduced-order aeroelastic models.
- Enable modeling of nonlinear dynamics across wide operating conditions for control applications.
Main Methods:
- Extended dynamic mode decomposition with control (DMDc) to handle algebraic equations.
- Developed an interpolation scheme to connect linear DMDc models from different regimes.
- Applied to a 3D numerical model of an airborne wind energy system.
Main Results:
- Accurate real-time prediction of nonlinear unsteady aeroelastic responses.
- Demonstrated enhanced model performance for model predictive control.
- Successfully modeled nonlinearities over multiple operating regimes.
Conclusions:
- The proposed framework enables accurate modeling of complex aeroelastic systems.
- Facilitates real-time prediction and control of flexible aerospace structures.
- Supports the adoption of advanced morphing wing technologies.
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