Print Email Facebook Twitter Black-box Online Aerodynamic Performance Optimization for a Seamless Wing with Distributed Morphing Title Black-box Online Aerodynamic Performance Optimization for a Seamless Wing with Distributed Morphing Author Ruland, O.L. (Student TU Delft) Mkhoyan, T. (TU Delft Arts & Crafts; TU Delft Aerospace Structures & Computational Mechanics) De Breuker, R. (TU Delft Aerospace Structures & Computational Mechanics) Wang, Xuerui (TU Delft Aerospace Structures & Computational Mechanics) Date 2022 Abstract Morphing is a promising bio-inspired technology, with the potential to make aircraft more economical and sustainable through adaptation of the wing shape for best efficiency at any flight condition. This paper proposes an online black-box performance optimization strategy for a seamless wing with distributed morphing control. Pursuing global performance, the presented method integrates a global radial basis function neural network (RBFNN) surrogate model with a derivative-free evolutionary optimization algorithm. The effectiveness of the optimization strategy was validated on a vortex lattice method (VLM) aerodynamic model of an over-actuated morphing wing augmented by wind tunnel experiment data. Simulations show that the proposed method is able to control the morphing shape and angle of attack to achieve various target lift coefficients with better aerodynamic efficiency than the unmorphed wing shape. The global nature of the on-board model allows the presented method to find shape solutions for a wide range of target lift coefficients without the need for additional model excitation maneuvers. Compared to the unmorphed shape, up to 14.6% of lift-to-drag ratio increase is achieved. To reference this document use: http://resolver.tudelft.nl/uuid:864e9a7e-2443-4077-87cb-2adfbb98e59e DOI https://doi.org/10.2514/6.2022-1840 ISBN 978-1-62410-631-6 Source AIAA SCITECH 2022 Forum Event AIAA SCITECH 2022 Forum, 2022-01-03 → 2022-01-07, virtual event Series AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022 Part of collection Institutional Repository Document type conference paper Rights © 2022 O.L. Ruland, T. Mkhoyan, R. De Breuker, Xuerui Wang Files PDF 6.2022_1840.pdf 5.19 MB Close viewer /islandora/object/uuid:864e9a7e-2443-4077-87cb-2adfbb98e59e/datastream/OBJ/view