Reinforcement learning has by now become well established in finding excellent flow control strategies for a variety of scenarios. Existing literature has focused on using a simple two-jet solution (and variants there-of) or a straightforward mean-centered multi-jet setup. This mean-centering approach is however non-injective in nature, such that distinct action predictions by the actor network can lead to the same implemented jet-intensities. Thus, the potential of true multi-jet setups still remains unexplored. To this end, in this study we first theoretically analyze multi-jet setups, highlighting the aforementioned pitfall and offer a viable alternative. We also derive upper-bounds on the running costs of these setups, and find the proposed approach to have a jet-count-independent maximum running cost (compared to a near-linear scaling for the traditional setup). The mean-centered and proposed multi-jet setups are applied to a variety of flow-configurations, to test performance and learning capabilities. The new formulation proves effective in learning more complex flow-control strategies, coordinating the jets in a sophisticated manner so as to produce favorable outcomes at minimal actuation cost. For the cylinder-in-channel case, this results in drag and total-force suppression to beyond an idealized symmetric case, whereas for the airfoil the separation region is minimized and significant improvements in aerodynamic efficiency are observed (from 53% up to 73% depending on jet configuration). Additionally, we also incorporate some best practices from traditional RL literature to show fast, reproducible and reliable learning, thereby bringing down the upfront training costs. This study thus provides a robust and mathematically grounded approach to multi-jet design and closes a hitherto overlooked theoretical gap.
Shocks Under Control: Taming Transonic Compressible Flow over an RAE2822 Airfoil with Deep Reinforcement Learning
Aerospace Engineering Department, Worcester Polytechnic Institute（航空航天工程系，沃斯特理工学院）；Department of Aerospace Engineering, University of Michigan（航空航天工程系，密歇根大学）
Minimal subspace rotation on the Stiefel manifold for stabilization and enhancement of projection-based reduced order models for the compressible Navier-Stokes equations
Active Control of Turbulent Airfoil Flows Using Adjoint-based Deep Learning
Graduate Student, Department of Aerospace and Mechanical Engineering（航空航天与机械工程系）；Postdoctoral Research Associate, Mathematical Institute（数学学院）；Assistant Professor, Department of Aerospace and Mechanical Engineering（航空航天与机械工程系）
Solving nonlinear subsonic compressible flow in infinite domain via multi-stage neural networks
Washington University in St. Louis（华盛顿大学圣路易斯分校）；University of Notre Dame（诺特大学）；Central South University（中南大学）；Stanford University（斯坦福大学）；New York University（纽约大学）
