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Found 4 papers, 2 papers with code
Reinforcement Learning for Active Flow Control in Experiments
We demonstrate experimentally the feasibility of applying reinforcement learning (RL) in flow control problems by automatically discovering active control strategies without any prior knowledge of the flow physics.
Fluid Dynamics Robotics
Fluid forces and vortex patterns of an oscillating cylinder pair in still water with both side-by-side and tandem configurations
In order to reveal the detailed flow physics that result in significant fluid forces alternations, the detailed flow visualization is provided by the numerical simulation: the small gap between two cylinders in a side-by-side configuration will result in a strong gap jet that enhances the energy dissipation and increase the drag, while due to the flow blocking effect for two cylinders in a tandem configuration, the drag coefficient decreases.
Fluid Dynamics
Physics-Informed Bayesian Learning of Electrohydrodynamic Polymer Jet Printing Dynamics
We demonstrate that the Machine Vision module can extract high-fidelity jet features in real-time from video data using an automated parallelized computer vision workflow.
How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning
The finding from this work can control hydrodynamic force on the operation of fluidic pinball system and potentially pave the way for exploring efficient active flow control strategies in other complex fluid dynamic problems.
