A Provably Robust Multi-Jet Framework applied to Active Flow Control of an Airfoil in Weakly Compressible Flow

TL;DR

This paper introduces a mathematically grounded, robust multi-jet reinforcement learning framework for active flow control, demonstrating improved aerodynamic performance and cost efficiency over traditional methods.

Contribution

It provides a theoretical analysis of multi-jet setups, proposes an alternative formulation, and demonstrates enhanced control capabilities with lower costs in flow management.

Findings

Achieved drag and force suppression beyond symmetric case in cylinder-in-channel.

Significant aerodynamic efficiency improvements on airfoil, up to 73%.

Proposed approach has jet-count-independent maximum running cost.

Abstract

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.