Extreme Aerodynamics: A Data-Driven Perspective

Abstract

While experiencing atmospheric turbulence on a commercial flight can be uncomfortable, it rarely compromises the stability of the aircraft. The situation is quite different for small air vehicles that operate in urban canyons, around mountainous terrains, and in the wakes of marine vessels, where they could encounter highly unsteady atmospheric conditions with relatively strong gusts. The spatiotemporal scales of such disturbances can be larger than the characteristic aerodynamic scales of the small vehicles, making the relative effect of disturbance significantly stronger than what a large commercial aircraft would experience. The gust ratio can exceed 1 in these extreme flight environments, making stable flight difficult, if not currently impossible. We refer to the study of aerodynamics for gust ratios over 1, extreme aerodynamics, and identify major challenges that require breakthroughs, particularly with data-driven approaches. Extreme aerodynamics present unique opportunities for innovative analysis techniques to study rich flow physics problems with strong nonlinearity, transient dynamics, and low-dimensional modeling over a large parameter space. Some of the approaches discussed herein should apply to a wider range of fluid dynamics problems with similar challenges.

Figures (5)

• Figure 1: Extreme aerodynamic flows with gust vortex impinging on a NACA 0012 airfoil at $Re = 100$. (a) Problem setup. A collection of lift traces and representative vorticity fields shown for (b) $\alpha = 20^\circ$ and (c) $\alpha = 30^\circ$. Figures adapted from Fukami:NC23.
• Figure 2: Compression of extreme aerodynamic flow snapshots using a convolutional neural network-based autoencoder Fukami:NC23. This network features observable augmentation using an auxiliary network on the side.
• Figure 3: Schematics of low-dimensional extreme aerodynamic attractors revealed from observable augmented convolutional neural network-based autoencoders for (a) vortex-airfoil interactions Fukami:NC23Fukami:JFM24, (b) transverse jet-airfoil interactions Smith:JFM24, and (c) plunging airfoil Odaka:AIAA25.
• Figure 4: Extreme vortex gust of $(G, d/c, h/c) = (2, 0.5, 0)$ hitting a square NACA 0015 wing at $\alpha = 14^\circ$ for $Re = 600$ and $10,000$Odaka:JFMXX2. Visualized are the three-dimensional vortical structures with $Q$-criteria ($Q = 100$, blue) and vorticity ($\| \omega \| = 7$, gray) isosurfaces. Corresponding lift histories are shown at the bottom.
• Figure 5: Extreme gust vortex responses by a thin (NACA 0006) and thick (NACA 0040) airfoils Lopez-Doriga:PRFXX. Vorticity fields are shown shortly after impact by a vortex gust of $(G,d/c,h/c,\alpha) = (2,1,-0.1,0^\circ)$. Corresponding lift histories are also shown.