Grasping extreme aerodynamics on a low-dimensional manifold

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, encountered in urban canyons, over mountainous terrains, and in ship wakes. With extreme weather becoming ever more frequent due to global warming, it is anticipated that aircraft, especially those that are smaller in size, will encounter sizeable atmospheric disturbances and still be expected to achieve stable flight. However, there exists virtually no theoretical fluid-dynamic foundation to describe the influence of extreme vortical gusts on wings. To compound this difficulty, there is a large parameter space for gust-wing interactions. While such interactions are seemingly complex and different for each combination of gust parameters, we show that the fundamental physics behind extreme aerodynamics is far simpler and lower-rank than traditionally expected. We reveal 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 compressed through machine learning into a low-dimensional manifold, which can enable real-time sparse reconstruction, dynamical modeling, and control of extremely unsteady gusty flows. The present findings offer support for the stable flight of next-generation small air vehicles in atmosphere conditions traditionally considered unflyable. In adverse weather, small-scale modern aircraft can encounter severe turbulence in urban canyons and mountainous areas hindering stable flight. The authors use machine learning to reveal the low-dimensional manifold that captures the extreme aerodynamics of gust-airfoil interactions.

Automated summary

As extreme weather conditions become more frequent, it is crucial for small air vehicles to achieve stable flight in the presence of atmospheric disturbances. However, there is a lack of theoretical understanding of the influence of extreme vortical gusts on wings. In this study, machine learning is used to reveal a low-dimensional manifold that captures the extreme aerodynamics of gust-airfoil interactions, enabling real-time reconstruction, modeling, and control of unsteady gusty flows. These findings provide support for the stable flight of next-generation small air vehicles in adverse weather conditions.