Title:
Development of a Multidisciplinary Design Analysis Framework for Unmanned Electric Flying Wings
Development of a Multidisciplinary Design Analysis Framework for Unmanned Electric Flying Wings
Author(s)
Whitmore, William Valentin
Advisor(s)
Mavris, Dimitri N.
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Abstract
Small-scale subsonic unmanned aerial vehicles have become common tools in both
military and civil applications. A vehicle configuration of special interest is the flying wing
(aka all-wing or tailless aircraft). This configuration can potentially reduce drag, increase
structural efficiency, and decrease detectability. When combined with an electric
propulsion system, it produces no observable emissions and possesses fewer maintenance
issues. Unfortunately, strong couplings between disciplinary analyses hinder the design of
unmanned electric flying wings. In particular, achieving adequate stability characteristics
degrades the aerodynamic efficiency of the vehicle, and constrains the available volume in
which subsystem components may be placed. Exploiting the potential advantages of
electric flying wings therefore necessitates a multidisciplinary perspective.
In order to overcome the identified challenges of unmanned electric flying wing
design, a multidisciplinary design analysis framework was conceptualized, implemented,
and evaluated. The Python-based framework synthesizes automated analysis modules that
model geometry, weight distribution, electric propulsion, aerodynamics, stability, and
performance. Virtual experiments demonstrated the framework’s utility in quickly
exploring a wide design space and assessing design robustness. Two important stand-alone
contributions developed for the framework are (1) an algorithm for densely packing battery
cells within a wing shape and (2) a parametric electric propulsion analysis code. In short,
the framework supports the design of small-scale (i.e. 0-55lb weight range) subsonic
unmanned electric flying wings with a host of valuable capabilities that were previously
unavailable within traditional design methods.
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Date Issued
2019-12-03
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