{
  "id": "2309.02462",
  "version": "v1",
  "published": "2023-09-04T13:30:29.000Z",
  "updated": "2023-09-04T13:30:29.000Z",
  "title": "Active flow control for three-dimensional cylinders through deep reinforcement learning",
  "authors": [
    "Pol Suárez",
    "Francisco Alcántara-Ávila",
    "Arnau Miró",
    "Jean Rabault",
    "Bernat Font",
    "Oriol Lehmkuhl",
    "R. Vinuesa"
  ],
  "comment": "ETMM14 2023 conference proceeding paper",
  "categories": [
    "physics.flu-dyn",
    "cs.LG"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-09-04T13:30:29.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "76F70",
      "I.2.0",
      "I.6.0"
    ],
    "keywords": [
      "active flow control",
      "three-dimensional cylinder",
      "deep reinforcement learning",
      "controlled zero-net-mass-flux synthetic jets",
      "independently controlled zero-net-mass-flux synthetic"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}