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Exploring Flapping Flight For Micro Aerial Vehicles


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Micro Aerial Vehicles and Animal Flight

Animal flight has undoubtedly played a significant role in many of the aeronautical engineering achievements of the past several centuries. Many of these impressive achievements have involved the pursuit of human air transport; however, as technology has continued advancing, uninhabited aerial vehicles (UAV's, also known as unmanned aerial vehicles) have received an increasing amount of attention.

Uninhabited micro aerial vehicles (MAV's) are considered useful for many diverse applications due to their relatively low cost (thousands to tens of thousands of dollars) and small size (wingspan < 15 cm, weight < 500g). Unfortunately flight behaviour changes as miniaturization pushes the boundaries of current designs. For example, the aerodynamic properties of small, bird-like designs are less predictable than those of high speed transport aircraft. As such, we use current engineering technology and turn to the birds, bats and insects for inspiration.

Our group is predominantly interested in simulating flapping wing flight using computational models of the fluid, structures and full morphing body dynamics. Our computational approach to understanding flapping flight characteristics is two pronged (the first, a top-down analysis of bat flight, the other ground-up design of flappers). The aim of the research is to use physical and biological models to drive the engineering design of these vehicles further.

Our primary computational work is part of a multi-institutional AFOSR MURI project (Brown University, Massachusetts Institute of Technology, University of Maryland, and Oregon State University). In this highly collaborative work, we examine bat flight for clues which illustrate how to design effective micro aerial vehicles. In addition, we also exploit a multifidelity computational design framework to understand and design flapping flight vehicles.

Our collaborators include:

The current project has been funded by several different sources at different times. We are very thankful to the following funding sources (over time):

Singapore-MIT Alliance (SMA), National Science Foundation (NSF -- This material is based upon work supported by the National Science Foundation under Grant No. 0540203 and 0540266. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.), Natural Science and Engineering Council of Canada (NSERC), and the Air Force Office of Scientific Research.


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Aerospace Computational Design Laboratory, Department of Aeronautics and Astronuatics. Massachusetts Institute of Technology.

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