Active Control of Flow over Rotating Cylinder by Multiple Jets using Deep Reinforcement Learning
Kamyar Dobakhti, Jafar Ghazanfarian

TL;DR
This paper demonstrates that combining rotation with deep reinforcement learning-controlled jets effectively suppresses vortex shedding, stabilizes flow, and reduces drag on a cylinder by nearly 50%, with optimized sensor and jet configurations.
Contribution
It introduces a novel approach integrating rotation with DRL for active flow control, optimizing jet and sensor placement for maximum drag reduction.
Findings
Drag coefficient reduced by up to 49.75%
Rotation combined with DRL stabilizes vortex shedding
Sensor placement impacts control effectiveness
Abstract
The real power of artificial intelligence appears in reinforcement learning, which is computationally and physically more sophisticated due to its dynamic nature. Rotation and injection are some of the proven ways in active flow control for drag reduction on blunt bodies. In this paper, rotation will be added to the cylinder alongside the deep reinforcement learning (DRL) algorithm, which uses multiple controlled jets to reach the maximum possible drag suppression. Characteristics of the DRL code, including controlling parameters, their limitations, and optimization of the DRL network for use with rotation will be presented. This work will focus on optimizing the number and positions of the jets, the sensors location, and the maximum allowed flow rate to jets in the form of the maximum allowed flow rate of each actuation and the total number of them per episode. It is found that…
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Taxonomy
TopicsFluid Dynamics and Turbulent Flows · Model Reduction and Neural Networks · Plasma and Flow Control in Aerodynamics
MethodsFocus
