Reconstructing the Features and Performances of Historic Airplanes

Martínez, Rodrigo Zapata; Pérez, Emilio Ortega; Palacín, José

doi:10.2514/6.2007-154

Cited by 1 publication

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“…In our study, V 2 design demonstrates the highest out‐of‐plane stiffness under pressure. Since the lift force on airfoils is significantly higher than drag force (the lift‐to‐drag ratio is commonly in the range of 10–20 [ 68 ] ) and the smallest dimension of the wing is its height, out‐of‐plane stiffness is the most critical stiffness among the others. It is worth mentioning that the previously‐reported curved rib design with a higher thickness (h = 3.85mm) also outperforms the basic design in out‐of‐plane stiffness.…”

Section: Resultsmentioning

confidence: 99%

Dragonfly‐Inspired Wing Design Enabled by Machine Learning and Maxwell's Reciprocal Diagrams

et al. 2023

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This research is taking the first steps toward applying a 2D dragonfly wing skeleton in the design of an airplane wing using artificial intelligence. The work relates the 2D morphology of the structural network of dragonfly veins to a secondary graph that is topologically dual and geometrically perpendicular to the initial network. This secondary network is referred as the reciprocal diagram proposed by Maxwell that can represent the static equilibrium of forces in the initial graph. Surprisingly, the secondary graph shows a direct relationship between the thickness of the structural members of a dragonfly wing and their in‐plane static equilibrium of forces that gives the location of the primary and secondary veins in the network. The initial and the reciprocal graph of the wing are used to train an integrated and comprehensive machine‐learning model that can generate similar graphs with both primary and secondary veins for a given boundary geometry. The result shows that the proposed algorithm can generate similar vein networks for an arbitrary boundary geometry with no prior topological information or the primary veins' location. The structural performance of the dragonfly wing in nature also motivated the authors to test this research's real‐world application for designing the cellular structures for the core of airplane wings as cantilever porous beams. The boundary geometry of various airplane wings is used as an input for the design proccedure. The internal structure is generated using the training model of the dragonfly veins and their reciprocal graphs. One application of this method is experimentally and numerically examined for designing the cellular core, 3D printed by fused deposition modeling, of the airfoil wing; the results suggest up to 25% improvements in the out‐of‐plane stiffness. The findings demonstrate that the proposed machine‐learning‐assisted approach can facilitate the generation of multiscale architectural patterns inspired by nature to form lightweight load‐bearable elements with superior structural properties.

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Section: Resultsmentioning

confidence: 99%