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Poster
Deep Dynamical Modeling and Control of Unsteady Fluid Flows
Jeremy Morton · Antony Jameson · Mykel J Kochenderfer · Freddie Witherden

Wed Dec 05 02:00 PM -- 04:00 PM (PST) @ Room 517 AB #149
in Wed Poster Session B »

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.

Keywords: Deep Autoencoders · Efficient Training Methods · Decision and Control
[ Paper]  [ Poster] 

Author Information

Jeremy Morton (Stanford University)
Antony Jameson (Texas A&M University)
Mykel J Kochenderfer (Stanford University)
Freddie Witherden (Imperial College London)

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