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Bioinspired morphing wings: mechanical design and wind tunnel experiments.

Lukas KilianFarzeen ShahidJing-Shan ZhaoChristian Navid Nayeri
Published in: Bioinspiration & biomimetics (2022)
Bioinspired morphing wings are part of a novel research direction offering greatly increased adaptability for use in unmanned aerial vehicles. Recent models published in the literature often rely on simplifications of the bird wing apparatus and fail to preserve many of the macroscopic morphological features. Therefore, a more holistic design approach could uncover further benefits of truly bioinspired bird wing models. With this issue in mind, a prototype inspired by crow wings ( Corvus genus) is developed, which is capable of planform wing morphing. The prototype imitates the feather structure of real birds and replicates the folding motion with a carbon fiber reinforced polymer skeleton with one controllable degree of freedom. The mechanism supplies a smooth airfoil lifting surface through a continuous morphing motion between a fully extended and a folded state. When extended, it has an elliptic planform and emarginated slots between primary remiges. In the folded state, the wingspan is reduced by 50% with a 40% reduction in surface area and the aspect ratio decreases from 2.9 to 1.2. Experimental data from a subsonic wind tunnel investigation is presented for flow velocities ranging from 5 to 20 m s -1 , corresponding to Reynolds numbers between 0.7 × 10 5 -2.8 × 10 5 . The wing is analyzed in the three static states (folded, intermediate, and extended) through aerodynamic coefficients and flow visualizations along the surface. The bioinspired design enables the wing to capture several phenomena found on real bird wings. Through its morphing capabilities and intrinsic softness, the wing can sustain large angles of attack with greatly delayed stall and maintain optimal performance at different velocities.
Keyphrases
  • systematic review
  • randomized controlled trial
  • machine learning
  • big data
  • mass spectrometry
  • deep learning