Concurrent Design and Flight Mission Optimization of Morphing Airborne Wind Energy Wings

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

doi:10.2514/1.j059621

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…In Figure 7 , the blue dash-dotted curve corresponds to the average fitness value f a , the black solid curve corresponds to the minimum fitness value f min , and finally the red dashed curve corresponds to f a − f min . Figure 7 illustrates that the diversity (measured by f a − f min ) decreases when SGA converges; thus, p c and p m should increase at early stage so as to increase the exploration capability ( Fasel et al, 2021 ). Nevertheless, extremely large values will counteract the algorithm converging to the global optimal ( Le Guisquet and Amabili, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

RETRACTED: Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

2021

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Aim: Multiple sclerosis (MS) is a disease, which can affect the brain and/or spinal cord, leading to a wide range of potential symptoms. This method aims to propose a novel MS recognition method.Methods: First, the bior4.4 wavelet is used to extract multiscale coefficients. Second, three types of biorthogonal wavelet features are proposed and calculated. Third, fitness-scaled adaptive genetic algorithm (FAGA)—a combination of standard genetic algorithm, adaptive mechanism, and power-rank fitness scaling—is harnessed as the optimization algorithm. Fourth, multiple-way data augmentation is utilized on the training set under the setting of 10 runs of 10-fold cross-validation. Our method is abbreviated as BWF-FAGA.Results: Our method achieves a sensitivity of 98.00 ± 0.95%, a specificity of 97.78 ± 0.95%, and an accuracy of 97.89 ± 0.94%. The area under the curve of our method is 0.9876.Conclusion: The results show that the proposed BWF-FAGA method is better than 10 state-of-the-art MS recognition methods, including eight artificial intelligence-based methods, and two deep learning-based methods.

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Section: Methodsmentioning

confidence: 99%

RETRACTED: Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

2021

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“…These flexible aero-structures pose a considerable modeling challenge, as they involve highly coupled and nonlinear interactions between the aerodynamic and structural dynamics. A range of aeroelastic modeling methods exist, from high-fidelity models able to simulate the coupled fluid flow fields and the deformation of the wing structure (Goza & Colonius, 2017;Mittal & Iaccarino, 2005;Peskin, 2002), to controloriented data-driven models including viscous and unsteady effects (Brunton et al, 2013;Hemati et al, 2017;Hickner et al, 2022), and lower-fidelity models such as doublet-lattice methods (Albano & Rodden, 1969), strip theory (Kim et al, 2008), and panel methods (Fasel et al, 2021;Fonzi et al, 2020;Murua et al, 2012). These methods provide accurate models of aeroelastic effects; however, computationally more efficient models are often necessary for optimization and control, even at the expense of some fidelity.…”

Section: Statement Of Needmentioning

confidence: 99%

FlexWing-ROM: A matlab framework for data-driven reduced-order modeling of flexible wings

Fasel¹,

Fonzi²,

Iannelli³

et al. 2022

JOSS

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Flexible wings pose a considerable modeling challenge, as they involve highly coupled and nonlinear interactions between the aerodynamic and structural dynamics. In this work, we provide an open source code framework, unifying recent data-driven modeling methods that extend the dynamic mode decomposition with control (DMDc) to model the nonlinear aeroelasticity of flexible wings. Our framework consists of (1) a fully parametrized flexible wing model; (2) a fluid-structure interaction (FSI) solver that couples a detailed finite element model of the wing structure with a 3D unsteady aerodynamic panel method; and (3) three different data-driven reduced-order aeroelastic modeling methods. We demonstrate our framework on two flexible wings and provide tutorials to compare the different data-driven methods. The code is widely applicable and useful for generating accurate and efficient data-driven reduced-order models, and it provides a benchmark for future developments of aeroelastic reduced-order modeling methods.

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“…Shape transformation enables structures to drastically alter their 3D shape and, therefore, it enables the design of multi‐functional components that can adapt to different environmental conditions and operate more efficiently. For example, adaptive facades have been investigated for increasing the energy efficiency of buildings, [ 1 , 2 , 3 ] morphing wings for more efficient flight performance, [ 4 , 5 , 6 ] and highly packageable components for better transportation. [ 7 , 8 ] Despite significant advancements, for actual applications, most concepts lack needed functionalities.…”

Section: Introductionmentioning

confidence: 99%

A Highly Multi‐Stable Meta‐Structure via Anisotropy for Large and Reversible Shape Transformation

2022

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Shape transformation offers the possibility of realizing devices whose 3D shape can be altered to adapt to different environments. Many applications would profit from reversible and actively controllable shape transformation together with a self‐locking capability. Solutions that combine such properties are rare. Here, a novel class of meta‐structures that can tackle this challenge is presented thanks to multi‐stability. Results demonstrate that the multi‐stability of the meta‐structure is strictly tied to the use of highly anisotropic materials. The design rules that enable large‐shape transformation, programmability, and self‐locking are derived, and it is proven that the shapes can be actively controlled and harnessed to realize inchworm‐inspired locomotion by strategically actuating the meta‐structure. This study provides routes toward novel shape adaptive lightweight structures where a metamaterial‐inspired assembly of anisotropic components leads to an unforeseen combination of properties, with potential applications in reconfigurable space structures, building facades, antennas, lenses, and soft robots.

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Concurrent Design and Flight Mission Optimization of Morphing Airborne Wind Energy Wings

Cited by 7 publications

References 38 publications

RETRACTED: Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

RETRACTED: Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

FlexWing-ROM: A matlab framework for data-driven reduced-order modeling of flexible wings

A Highly Multi‐Stable Meta‐Structure via Anisotropy for Large and Reversible Shape Transformation

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