Data-driven transient lift attenuation for extreme vortex gust-airfoil interactions

Abstract

We present a data-driven feedforward control to attenuate large transient lift experienced by an airfoil disturbed by an extreme level of discrete vortex gust. The current analysis uses a nonlinear machine-learning technique to compress the high-dimensional flow dynamics onto a low-dimensional manifold. While the interaction dynamics between the airfoil and extreme vortex gust are parameterized by its size, gust ratio, and position, the wake responses are well-captured on this simple manifold. The effect of extreme vortex disturbance about the undisturbed baseline flows can be extracted in a physically-interpretable manner. Furthermore, we call on phase-amplitude reduction to model and control the complex nonlinear extreme aerodynamic flows. The present phase-amplitude reduction model reveals the sensitivity of the dynamical system in terms of the phase shift and amplitude change induced by external forcing with respect to the baseline periodic orbit. By performing the phase-amplitude analysis for a latent dynamical model identified by sparse regression, the sensitivity functions of low-dimensionalized aerodynamic flows for both phase and amplitude are derived. With the phase and amplitude sensitivity functions, optimal forcing can be determined to quickly suppress the effect of extreme vortex gusts towards the undisturbed states in a low-order space. The present optimal flow modification built upon the machine-learned low-dimensional subspace quickly alleviates the impact of transient vortex gusts for a variety of extreme aerodynamic scenarios, providing a potential foundation for flight of small-scale air vehicles in adverse atmospheric conditions.

To appear in Journal of Fluid Mechanics

References in corpus (35)

• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Machine Learning for Fluid Mechanics
• Super-resolution reconstruction of turbulent flows with machine learning
• Phase reduction approach to synchronization of nonlinear oscillators
• Shallow Neural Networks for Fluid Flow Reconstruction with Limited Sensors
• Isostables, isochrons, and Koopman spectrum for the action-angle representation of stable fixed point dynamics
• Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows
• Multi-level Convolutional Autoencoder Networks for Parametric Prediction of Spatio-temporal Dynamics
• Sparse identification of nonlinear dynamics with low-dimensionalized flow representations
• Grasping Extreme Aerodynamics on a Low-Dimensional Manifold
• Phase-amplitude reduction of transient dynamics far from attractors for limit-cycling systems
• Optimal waveform for the entrainment of a weakly forced oscillator
• Phase-amplitude descriptions of neural oscillator models
• Fluid flow around NACA 0012 airfoil at low-Reynolds numbers with hybrid lattice Boltzmann method
• On the Concept of Dynamical Reduction : The Case of Coupled Oscillators
• Phase Reduction Method for Strongly Perturbed Limit Cycle Oscillators
• Phase-response analysis of synchronization for periodic flows
• Sparse sensor reconstruction of vortex-impinged airfoil wake with machine learning
• Global computation of phase-amplitude reduction for limit-cycle dynamics
• Networked oscillator based modeling and control of unsteady wakes
• Data-driven low-dimensional dynamic model of Kolmogorov flow
• Data-driven nonlinear turbulent flow scaling with Buckingham Pi variables
• Contribution of spanwise and cross-span vortices to the lift generation of low-aspect-ratio wings: Insights from force partitioning
• A cyclic perspective on transient gust encounters through the lens of persistent homology
• Phase-based control of periodic fluid flows
• Phase-synchronization properties of laminar cylinder wake for periodic external forcings
• Nonlinear phase-amplitude reduction of delay-induced oscillations
• Optimal waveform for fast synchronization of airfoil wakes
• Jacobian-free algorithm to calculate the phase sensitivity function in the phase reduction theory and its applications to Kármán's vortex street
• Phase-locking of laminar wake to periodic vibrations of a circular cylinder
• Adjoint-based phase reduction analysis of incompressible periodic flows
• Image and video compression of fluid flow data
• Phase and amplitude description of complex oscillatory patterns in reaction-diffusion systems
• Phase-Amplitude Reduction and Optimal Phase Locking of Collectively Oscillating Networks
• Two-dimensional hydrodynamic simulation for synchronization in coupled density oscillators

Cited by in corpus (14)

• Compressing fluid flows with nonlinear machine learning: mode decomposition, latent modeling, and flow control
• Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraint
• Information-theoretic machine learning for time-varying mode decomposition of separated aerodynamic flows
• Setting of the Poincaré section for accurately calculating the phase of rhythmic spatiotemporal dynamics
• Compact representation of transonic airfoil buffet flows with observable-augmented machine learning
• Phase autoencoder for rapid data-driven synchronization of rhythmic spatiotemporal patterns
• Vortical similarities across laminar and turbulent extreme gust encounters
• Phase-based analysis and control of low Reynolds number aeroelastic flows
• Convolutional causal learning for aerodynamic flows
• Multi-resolution Physics-Aware Recurrent Convolutional Neural Network for Complex Flows
• Extreme vortex gust encounters by a square wing
• Phase reduction analysis of traveling breathers in reaction--diffusion systems
• VIVALDy: A Hybrid Generative Reduced-Order Model for Turbulent Flows, Applied to Vortex-Induced Vibrations
• Dynamic mode decomposition for detecting oscillatory transient activity via sparsity and smoothness regularization