Cargando…

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

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 perp...

Descripción completa

Detalles Bibliográficos
Autores principales: Zheng, Hao, Mofatteh, Hossein, Hablicsek, Marton, Akbarzadeh, Abdolhamid, Akbarzadeh, Masoud
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288228/
https://www.ncbi.nlm.nih.gov/pubmed/37119466
http://dx.doi.org/10.1002/advs.202207635
_version_ 1785062036757020672
author Zheng, Hao
Mofatteh, Hossein
Hablicsek, Marton
Akbarzadeh, Abdolhamid
Akbarzadeh, Masoud
author_facet Zheng, Hao
Mofatteh, Hossein
Hablicsek, Marton
Akbarzadeh, Abdolhamid
Akbarzadeh, Masoud
author_sort Zheng, Hao
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10288228
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-102882282023-06-24 Dragonfly‐Inspired Wing Design Enabled by Machine Learning and Maxwell's Reciprocal Diagrams Zheng, Hao Mofatteh, Hossein Hablicsek, Marton Akbarzadeh, Abdolhamid Akbarzadeh, Masoud Adv Sci (Weinh) Research Articles 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. John Wiley and Sons Inc. 2023-04-29 /pmc/articles/PMC10288228/ /pubmed/37119466 http://dx.doi.org/10.1002/advs.202207635 Text en © 2023 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Zheng, Hao
Mofatteh, Hossein
Hablicsek, Marton
Akbarzadeh, Abdolhamid
Akbarzadeh, Masoud
Dragonfly‐Inspired Wing Design Enabled by Machine Learning and Maxwell's Reciprocal Diagrams
title Dragonfly‐Inspired Wing Design Enabled by Machine Learning and Maxwell's Reciprocal Diagrams
title_full Dragonfly‐Inspired Wing Design Enabled by Machine Learning and Maxwell's Reciprocal Diagrams
title_fullStr Dragonfly‐Inspired Wing Design Enabled by Machine Learning and Maxwell's Reciprocal Diagrams
title_full_unstemmed Dragonfly‐Inspired Wing Design Enabled by Machine Learning and Maxwell's Reciprocal Diagrams
title_short Dragonfly‐Inspired Wing Design Enabled by Machine Learning and Maxwell's Reciprocal Diagrams
title_sort dragonfly‐inspired wing design enabled by machine learning and maxwell's reciprocal diagrams
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288228/
https://www.ncbi.nlm.nih.gov/pubmed/37119466
http://dx.doi.org/10.1002/advs.202207635
work_keys_str_mv AT zhenghao dragonflyinspiredwingdesignenabledbymachinelearningandmaxwellsreciprocaldiagrams
AT mofattehhossein dragonflyinspiredwingdesignenabledbymachinelearningandmaxwellsreciprocaldiagrams
AT hablicsekmarton dragonflyinspiredwingdesignenabledbymachinelearningandmaxwellsreciprocaldiagrams
AT akbarzadehabdolhamid dragonflyinspiredwingdesignenabledbymachinelearningandmaxwellsreciprocaldiagrams
AT akbarzadehmasoud dragonflyinspiredwingdesignenabledbymachinelearningandmaxwellsreciprocaldiagrams