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About Artificial Neural Networks Trained Through Deep Reinforcement Learning Discover Control Strategies For Active Flow Control
This document summarizes a study where researchers used an artificial neural network trained through deep reinforcement learning to perform active flow control. The neural network learned to stabilize vortex shedding and reduce drag in a 2D simulation of flow past a cylinder at a Reynolds number of 100 by controlling the mass flow rates of two jets on the cylinder sides. It successfully stabilized the wake and reduced drag by around 8% while using small jet flow rates equivalent to 0.5% of the cylinder cross-section flow. This opens up a new method for active flow control using neural networks.
Detailed Information
| Author: | ['Khoi Le'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 22 |
| Language: | English |
| Format: | |
| Price: | FREE |
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