Deep Dynamical Modeling and Control of Unsteady Fluid Flows
This work addresses the problem of controlling complex fluid flows for applications in engineering and fluid dynamics, representing an incremental advance by applying Koopman theory to a specific domain.
The paper tackled the challenge of designing flow control systems for unsteady fluid flows by learning dynamical models from CFD data, resulting in stable models that enabled model predictive control to suppress vortex shedding with an interpretable control law.
The design of flow control systems remains a challenge due to the nonlinear nature of the equations that govern fluid flow. However, recent advances in computational fluid dynamics (CFD) have enabled the simulation of complex fluid flows with high accuracy, opening the possibility of using learning-based approaches to facilitate controller design. We present a method for learning the forced and unforced dynamics of airflow over a cylinder directly from CFD data. The proposed approach, grounded in Koopman theory, is shown to produce stable dynamical models that can predict the time evolution of the cylinder system over extended time horizons. Finally, by performing model predictive control with the learned dynamical models, we are able to find a straightforward, interpretable control law for suppressing vortex shedding in the wake of the cylinder.