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Modern air vehicles perform a wide range of operations, including
transportation, defense, surveillance, and rescue. These aircraft can fly in
calm conditions but avoid operations in gusty environments, which are seen in
urban canyons, over mountainous terrains, and in ship wakes. Smaller aircraft
are especially prone to such gust disturbances. With extreme weather becoming
ever more frequent due to global warming, it is anticipated that aircraft,
especially those that are smaller in size, encounter large-scale atmospheric
disturbances and still be expected to manage stable flight. However, there
exists virtually no foundation to describe the influence of extreme vortical
gusts on flying bodies. To compound on this difficult problem, there is an
enormous parameter space for gusty conditions wings encounter. While the
interaction between the vortical gusts and wings is seemingly complex and
different for each combination of gust parameters, we show in this study that
the fundamental physics behind extreme aerodynamics is far simpler and low-rank
than traditionally expected. It is revealed that the nonlinear vortical flow
field over time and parameter space can be compressed to only three variables
with a lift-augmented autoencoder while holding the essence of the original
high-dimensional physics. Extreme aerodynamic flows can be optimally compressed
through machine learning into a low-dimensional manifold, implying that the
identification of appropriate coordinates facilitates analyses, modeling, and
control of extremely unsteady gusty flows. The present findings support the
stable flight of next-generation small air vehicles in atmosphere conditions
traditionally considered unflyable.
