Robust flow control and optimal sensor placement using deep reinforcement learning

2020/06/19 by Romain Paris, Samir Beneddine, Paris, Romain +3 · 14 citations
Engineering · Physics and Astronomy · #Fluid Dynamics and Turbulent Flows #Model Reduction and Neural Networks #Plasma and Flow Control in Aerodynamics

paper · doi:10.1017/jfm.2020.1170

Abstract

Abstract

Citations

• Closed-Loop Turbulence Control: Progress and Challenges
• Dynamics and Control of Global Instabilities in Open-Flows: A Linearized Approach
• Linear Closed-Loop Control of Fluid Instabilities and Noise-Induced Perturbations: A Review of Approaches and Tools1
• Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control

Cited by

• Deep Reinforcement Learning for Active Flow Control around a Circular Cylinder Using Unsteady-mode Plasma Actuators
• Using reinforcement learning to probe the role of feedback in skill acquisition
• Observer-based neural networks for flow estimation and control
• Deep Reinforcement Learning in Action: Real-Time Control of Vortex-Induced Vibrations
• Real-time reinforcement learning for turbulent state-dependent control in a bluff-body wake
• Towards Agent-based Test Support Systems: An Unsupervised Environment Design Approach
• Domain Adaptation of Drag Reduction Policy to Partial Measurements
• Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms. [europepmc]
• Efficient Sensor Node Selection for Observability Gramian Optimization. [europepmc]
• Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning. [europepmc]
• Sensor-Driven Surrogate Modeling and Control of Nonlinear Dynamical Systems Using FAE-CAE-LSTM and Deep Reinforcement Learning. [europepmc]
• Multi-agent Reinforcement Learning for the Control of Three-Dimensional Rayleigh-Bénard Convection. [europepmc]
• Reinforcement Learning of Chaotic Systems Control in Partially Observable Environments. [europepmc]
• Deep Reinforcement Learning for the Management of the Wall Regeneration Cycle in Wall-Bounded Turbulent Flows. [europepmc]

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• Optimal sensor placement using machine learning
• Measurement Optimization under Uncertainty using Deep Reinforcement Learning
• Beacon, a lightweight deep reinforcement learning benchmark library for flow control
• Deep Reinforcement Learning in Fluid Mechanics: a promising method for both Active Flow Control and Shape Optimization
• Optimizing Flow Control with Deep Reinforcement Learning: Plasma Actuator Placement around a Square Cylinder