This work presents an integral framework to derive aeroelastic models for very flexible aircraft that can be used in design routines, operational envelope analysis and control applications. Aircraft are modelled using a nonlinear geometrically-exact beam model coupled with an Unsteady Vortex-Lattice Method aerodynamic solver, capable of capturing important nonlinear couplings and effects that significantly impact the flight characteristics of very flexible aircraft. Then, complete linearised expressions of the aircraft system about trim reference conditions at possibly large deformations are presented. The nature of the aerodynamic models results in a high-dimensional system that requires of model reduction methods for efficient analysis and manipulation.
Krylov-subspace model reduction methods are implemented to reduce the dimensionality of the multi-input multi-output linearised aerodynamic model and achieve a very significant reduction in the size of the size of the system. The reduced aerodynamic model is then coupled with a modal expression of the linearised beam model, resulting in a compact aeroelastic state-space that can be efficiently used on desktop hardware for linear analysis or as part of internal control models. These have been used to explore the design space of a very flexible wing with complex aeroelastic properties to determine the flutter boundaries, for which experimental data has become available that validates the methods presented herein. Additionally, they have been integrated in a model predictive control framework, where the reduced linear aerodynamic model is part of the control model, and the simulation plant is the nonlinear flight dynamic/aeroelastic model connected as a hardware-in-the-loop platform.
Finally, in order to accelerate the design space exploration of very flexible structures, state-space interpolation methods are sought to obtain, with a few linearised models sampled across the domain, interpolated state-spaces anywhere in the parameter-space in a fast and accurate manner. The performance of the interpolation schemes is heavily dependent on the location of the sampling points on the design space, therefore, a novel adaptive Bayesian sampling scheme is presented to choose these points in an optimal approach that minimises the interpolation error function.Open Acces
