Grasping Extreme Aerodynamics on a Low-Dimensional Manifold

Abstract

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.

References in corpus (4)

• Grasping Extreme Aerodynamics on a Low-Dimensional Manifold
• Fluid flow around NACA 0012 airfoil at low-Reynolds numbers with hybrid lattice Boltzmann method
• Sparse sensor reconstruction of vortex-impinged airfoil wake with machine learning
• A cyclic perspective on transient gust encounters through the lens of persistent homology

Cited by in corpus (21)

• Grasping Extreme Aerodynamics on a Low-Dimensional Manifold
• Data-driven transient lift attenuation for extreme vortex gust-airfoil interactions
• A cyclic perspective on transient gust encounters through the lens of persistent homology
• Compressing fluid flows with nonlinear machine learning: mode decomposition, latent modeling, and flow control
• Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure Measurements
• Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraint
• Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows
• Decoder Decomposition for the Analysis of the Latent Space of Nonlinear Autoencoders With Wind-Tunnel Experimental Data
• Generative prediction of flow fields around an obstacle using the diffusion model
• Bayesian inference of vorticity in unbounded flow from limited pressure measurements
• Information-theoretic machine learning for time-varying mode decomposition of separated aerodynamic flows
• Compact representation of transonic airfoil buffet flows with observable-augmented machine learning
• Phase autoencoder for rapid data-driven synchronization of rhythmic spatiotemporal patterns
• Super-resolution reconstruction of turbulent flows from a single Lagrangian trajectory
• Vortical similarities across laminar and turbulent extreme gust encounters
• Convolutional causal learning for aerodynamic flows
• Extreme vortex gust encounters by a square wing
• Multi-resolution Physics-Aware Recurrent Convolutional Neural Network for Complex Flows
• VIVALDy: A Hybrid Generative Reduced-Order Model for Turbulent Flows, Applied to Vortex-Induced Vibrations
• A data-driven approach for modeling large-amplitude flow-induced oscillations of elastically mounted pitching wings
• Spatiotemporal System Forecasting with Irregular Time Steps via Masked Autoencoder