Reduced-order modeling of fluids systems, with applications in unsteady aerodynamics

Abstract

This thesis focuses on two major themes: modeling and understanding the dynamics of rapidly pitching airfoils, and developing methods that can be used to extract models and pertinent features from datasets obtained in the study of these and other systems in fluid mechanics and aerodynamics. Much of the work utilizes in some capacity dynamic mode decomposition (DMD), a recently developed method to extract dynamical features and models from data.

The investigation of pitching airfoils includes both wind tunnel experiments and direct numerical simulations. Experiments are performed on a NACA 0012 airfoil undergoing rapid pitching motion, with the focus on developing a switched linear modeling framework that can accurately predict unsteady aerodynamic forces and pressure distributions throughout arbitrary pitching motions.

Numerical simulations are used to study the behavior of sinusoidally pitching airfoils. By systematically varying the amplitude, frequency, mean angle and axis of pitching, a comprehensive database of results is acquired, from which interesting regions in parameter space are identified and studied. Attention is given to pitching at "preferred" frequencies, where vortex shedding in the wake is excited or amplified, leading to larger lift forces.

More generally, the ability to extract nonlinear models that describe the behavior of complex fluids systems can assist in not only understanding the dominant features of such systems, but also to achieve accurate prediction and control. One potential avenue to achieve this objective is through numerical approximation of the Koopman operator, an infinite-dimensional linear operator capable of describing finite-dimensional nonlinear systems, such as those that might describe the dominant dynamics of fluids systems. This idea is explored by showing that algorithms designed to approximate the Koopman operator can indeed be utilized to accurately model nonlinear fluids systems, even when the data available is limited or noisy.

Data-driven algorithms can be adversely affected by noisy data. Focusing on DMD, it is shown analytically that the algorithm is biased to sensor noise, which explains a previously observed sensitivity to noisy data. Using this finding, a number of modifications to DMD are proposed, which all give better approximations of the true dynamics using noise-corrupted data.