Active Control of Flow over Rotating Cylinder by Multiple Jets using Deep Reinforcement Learning

2023-07-22 · Kamyar Dobakhti, Jafar Ghazanfarian

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 combining the rotation and DRL is promising since it suppresses the vortex shedding, stabilizes the Karman vortex street, and reduces the drag coefficient by up to 49.75%. Also, it will be shown that having more sensors at more locations is not always a good choice and the sensor number and location should be determined based on the need of the user and corresponding configuration. Also, allowing the agent to have access to higher flow rates, mostly reduces the performance, except when the cylinder rotates. In all cases, the agent can keep the lift coefficient at a value near zero, or stabilize it at a smaller number.

📄 PDF Abstract BibTeX arXiv:2307.12083

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement Learningreinforcement-learning

Methods 이 논문이 사용한 방법론

Focus 설명 없음

How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning

2023-04-23 · Haodong Feng, Yue Wang, Hui Xiang, Zhiyang Jin 외

Deep reinforcement learning (DRL) for fluidic pinball, three individually rotating cylinders in the uniform flow arranged in an equilaterally triangular configuration, can learn the efficient flow control strategies due …

Decision Making Deep Reinforcement Learning reinforcement-learning Self-Learning+2

Active flow control for three-dimensional cylinders through deep reinforcement learning

2023-09-04 · Pol Suárez, Francisco Alcántara-Ávila, Arnau Miró, Jean Rabault 외

This paper presents for the first time successful results of active flow control with multiple independently controlled zero-net-mass-flux synthetic jets. The jets are placed on a three-dimensional cylinder along its spa…

Deep Reinforcement Learning Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Towards Active Flow Control Strategies Through Deep Reinforcement Learning

2024-11-08 · Ricard Montalà, Bernat Font, Pol Suárez, Jean Rabault 외

This paper presents a deep reinforcement learning (DRL) framework for active flow control (AFC) to reduce drag in aerodynamic bodies. Tested on a 3D cylinder at Re = 100, the DRL approach achieved a 9.32% drag reduction …

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning

Dynamic Feature-based Deep Reinforcement Learning for Flow Control of Circular Cylinder with Sparse Surface Pressure Sensing

2023-07-05 · Qiulei Wang, Lei Yan, Gang Hu, Wenli Chen 외

This study proposes a self-learning algorithm for closed-loop cylinder wake control targeting lower drag and lower lift fluctuations with the additional challenge of sparse sensor information, taking deep reinforcement l…

Deep Reinforcement Learning Self-Learning

Deep Dynamical Modeling and Control of Unsteady Fluid Flows

2018-05-18 · NeurIPS 2018 12 · Jeremy Morton, Freddie D. Witherden, Antony Jameson, Mykel J. Kochenderfer

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 co…

Model Predictive Control