Biomimetics in Aerospace Engineering

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 8421

Special Issue Editor

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Guest Editor
Department of Mechanical Engineering, New Mexico Tech, Weir Hall, Room 208, Socorro, NM 87801, USA
Interests: drones (UAV/MAV/NAV/PAV): fixed wings, flapping wings, tilt-rotor/wing drones, morphing drones, space and marine drones
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Special Issue Information

Dear Colleagues,

During millions of years of evolution, nature has developed processes, objects, materials, and functions to increase efficiency. Through biomimicry and inspiration from this treasure trove, engineers and biologists have become interested in learning from biological insights. Sometimes, looking at nature provides us with the best answers for the development and optimization of different types of systems, including aerospace systems. Nature always has effective solutions for many complex tasks in aerospace industries, such as drag reduction techniques, locomotion, navigation, control, sensing, and aircraft design. The growing science of biomimicry focuses on the numerous things engineers can learn about efficient solutions for aerospace systems that nature has spent millions of years refining. As an example, one of the interesting aspects of avian flight dynamics is how natural flyers, such as birds and insects, can deform their shape to optimize their flight in different flight modes. Therefore, the concept of morphing structures for aerial vehicles can be traced to the observation of birds as they fly.

This Special Issue invites submissions that apply the knowledge learned and inspired by nature and biological systems to solve concrete aerospace engineering problems, including but not limited to:

  • Bioinspired morphing design for aerial systems;
  • Biomimetics or bioinspired approaches for aerial systems drag reduction;
  • Bioinspired drones/UAVs;
  • Avian/insects and bioinspired flying systems;
  • Bioinspired materials for aerospace structures;
  • Bioinspired or biomimetics control techniques for aerospace systems;
  • Bioinspired or biomimetics multisensory navigation;
  • Bioinspired or biomimetics propulsion systems for aerial vehicles;
  • Bioinspired flocking and swarming flight;
  • Bioinspired amphibious systems;
  • Bioinspired antipredator adoption mechanisms for aerial systems.

Prof. Dr. Mostafa Hassanalian
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Bioinspired drag reduction techniques
  • Biomimetics and flight efficiency
  • Bioinspired aerial vehicle design
  • Bioinspired materials for aerospace structures
  • Bioinspired aerodynamics
  • Bioinspired propulsion
  • Avian and swarming flight
  • Biomimicry drones/UAVs/ flapping wings
  • Bioinspired navigation and control

Published Papers (1 paper)

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22 pages, 11944 KiB  
Minimum-Cost Drone–Nest Matching through the Kuhn–Munkres Algorithm in Smart Cities: Energy Management and Efficiency Enhancement
by Amir Mirzaeinia and Mostafa Hassanalian
Aerospace 2019, 6(11), 125; - 17 Nov 2019
Cited by 35 | Viewed by 6593
The development of new concepts for smart cities and the application of drones in this area requires different architecture for the drones’ stations (nests) and their placement. Drones’ stations are designed to protect drones from hazards and utilize charging mechanisms such as solar [...] Read more.
The development of new concepts for smart cities and the application of drones in this area requires different architecture for the drones’ stations (nests) and their placement. Drones’ stations are designed to protect drones from hazards and utilize charging mechanisms such as solar cells to recharge them. Increasing the number of drones in smart cities makes it harder to find the optimum station for each drone to go to after performing its mission. In classic ordered technique, each drone returns to its preassigned station, which is shown to be not very efficient. Greedy and Kuhn–Munkres (Hungarian) algorithms are used to match the drone to the best nesting station. Three different scenarios are investigated in this study; (1) drones with the same level of energy, (2) drones with different levels of energy, and (3) drones and stations with different levels of energy. The results show that an energy consumption reduction of 25–80% can be achieved by applying the Kuhn–Munkres and greedy algorithms in drone–nest matching compared to preassigned stations. A graphical user interface is also designed to demonstrate drone–station matching through the Kuhn–Munkres and greedy algorithms. Full article
(This article belongs to the Special Issue Biomimetics in Aerospace Engineering)
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