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

arXiv: https://arxiv.org/abs/2309.02462 · HTML: https://mlsanity.com/paper/2309.02462/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 1 of 1)

- [Effective control of two-dimensional Rayleigh--B'enard convection: invariant multi-agent reinforcement learning is all you need](https://mlsanity.com/paper/2304.02370/) (2023)

## Built on by (most cited first, 3 of 3)

- [Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality](https://mlsanity.com/paper/2407.17822/) (2024)
- [Navigation in a simplified Urban Flow through Deep Reinforcement Learning](https://mlsanity.com/paper/2409.17922/) (2024)
- [Towards Active Flow Control Strategies Through Deep Reinforcement Learning](https://mlsanity.com/paper/2411.05536/) (2024)

## Abstract (arXiv)

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 span with the aim of reducing the drag coefficient. The method is based on a deep-reinforcement-learning framework that couples a computational-fluid-dynamics solver with an agent using the proximal-policy-optimization algorithm. We implement a multi-agent reinforcement-learning framework which offers numerous advantages: it exploits local invariants, makes the control adaptable to different geometries, facilitates transfer learning and cross-application of agents and results in significant training speedup. In this contribution we report significant drag reduction after applying the DRL-based control in three different configurations of the problem.