Reduced-Order Dynamic Model of a Morphing Airborne Wind Energy Aircraft

Fasel, Urban; Tiso, Paolo; Keidel, Dominic; Molinari, Giulio; Ermanni, Paolo

doi:10.2514/1.j058019

Cited by 16 publications

(23 citation statements)

References 47 publications

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“…The BMD algorithm will be applied in Section IV to the FSI solver developed in [18], which presents the algebraic constraints described in Section II.B. It is thus presented here an extension of the algorithm discussed in the previous section to handle this instance.…”

Section: Extension To Handle Algebraic Constraintsmentioning

confidence: 99%

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

Iannelli

Fasel

Yogarajah

et al. 2021

AIAA Scitech 2021 Forum

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The paper proposes a novel approach to data-driven reduced-order modeling which combines the Dynamic Mode Decomposition technique with the concept of balanced realization. The information on the system comes from input, state, and output trajectories, and the goal is to derive a linear low-dimensional input-output model approximation. Since the dynamics of aerospace systems markedly changes when some parameters are varied, it is desirable to capture this feature in the system's description. Therefore, a Linear Parameter-Varying representation made of a collection of state-consistent linear time-invariant reduced-order models is sought. The main technical novelty of the proposed algorithm consists of replacing the orthogonal projection onto the POD modes, typical of Dynamic Mode Decomposition techniques, with a balancing oblique projection. The advantages are that the input-output information in the lower-dimensional representation is maximized, and that a parameter-varying projection is possible while also achieving state-consistency. The validity of the proposed approach is demonstrated on a morphing wing for airborne wind energy applications by comparing its prediction capabilities with those of a recent algorithm from the literature.

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Section: Extension To Handle Algebraic Constraintsmentioning

confidence: 99%

“…The algorithms are tested on a high-fidelity, fluid-structure interaction (FSI) numerical model of an airborne wind energy (AWE) morphing wing. The FSI simulator is described in [18] and the wing was analyzed in detail in [19].…”

Section: Introductionmentioning

confidence: 99%

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

Iannelli

Fasel

Yogarajah

et al. 2021

AIAA Scitech 2021 Forum

Self Cite

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“…First, an extension of the DMD method is developed for algebraic differential equations to generate a reduced-order unsteady aerodynamic and aeroelastic model. To generate the ROM, the algorithm sequentially applies a mode superposition method on a detailed three-dimensional (3D) structural finite-element model [30], followed by using the extended algebraic DMD method on a coupled structural and unsteady 3D panel method [31,32]. Second, an interpolation scheme smoothly connecting multiple ROMs valid at different flight velocities is introduced.…”

Section: Introductionmentioning

confidence: 99%

“…However, more refined models are required in the case of morphing and geometrically complex wings that exhibit flexibility in the chordwise direction and, therefore, strongly interact with the aerodynamics. Detailed finite-element models, based on both beam and shell elements, are therefore used to accurately represent the characteristics of such structures [31]. To increase computational efficiency, model reduction based on modal decomposition techniques are often applied [30,31].…”

Section: Introductionmentioning

confidence: 99%

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Data-driven nonlinear aeroelastic models of morphing wings for control

2020

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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.

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