Network-Based Feedback Control of Fluid Flows
Abstract
Unsteady fluid flows have complex dynamics due to the nonlinear interactions amongst vortical elements. In this report, a network-theoretic framework is developed to describe vortical and modal (coherent structure) interactions in unsteady fluid flows. A sparsified dynamics model and a networked-oscillator model describe the complex dynamics in fluid flows in terms of vortical and modal networks, respectively. Based on the characterized network interactions, model-based feedback control laws are established, particularly for controlling the flow unsteadiness. Furthermore, to characterize model-free feedback control laws for suppressing flow separation in turbulent flows, a data-driven approach leveraging unsupervised clustering is developed. This approach alters the Markov transition dynamics of fluid flow trajectories in an optimal manner using a cluster-based control strategy. To describe vortical interactions, dense fluid flow graphs are constructed using discrete point vortices as nodes and induced velocity as edge weights. Sparsification techniques are then employed on these graph representations based on spectral graph theory to construct sparse graphs of the overall vortical interactions which maintain similar spectral properties as the original setup. Utilizing the sparse vortical graphs, a sparsified-dynamics model is developed which drastically reduces the computational cost to predict the dynamical behavior of vortices, sharing characteristics of reduced-order models.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 31, 2022
- Accession Number
- AD1230021
Entities
People
- Aditya Nair
- Chi-an Yeh
- Chiang Shih
- Jared Callaham
- Steven Brunton
- Taira Kunihiko
- Zhe Bai
Organizations
- Florida State University
- University of California, Los Angeles
- University of Washington