Resolvent-Based Estimation for Control of Turbulent Aerodynamic Flows
Abstract
In this project, we developed, implemented, and demonstrated an optimal resolvent-based estimation and control framework for turbulent aerodynamic flow fields using limited measurements on the surface of an aircraft. While state estimation and control are classical topics in dynamical systems and control theory, standard methods have several disadvantages when applied to turbulent flows, including high costs and restrictions in their ability to incorporate key physics. We overcame these limitations by leveraging state-of-the-art models for coherent structures based on resolvent analysis. Specifically, we derived an optimal resolvent-based estimator and controller with several advantages over standard methods. When equivalent assumptions are made, the resolvent-based estimator and controller reproduce the classical Kalman filter and LQG controller, respectively, but at substantially lower computational cost using an efficient time-stepping method for constructing the kernels. Unlike these standard methods, the resolvent-based approach can naturally accommodate forcing terms (nonlinear terms from Navier-Stokes) with colored-in-time statistics, significantly improving the accuracy of the methods. When desired, causality is optimally enforced via a Wiener-Hopf decomposition.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 30, 2024
- Accession Number
- AD1230458
Entities
People
- Aaron Towne