تقرير
Active flow control for three-dimensional cylinders through deep reinforcement learning
| العنوان: | Active flow control for three-dimensional cylinders through deep reinforcement learning |
|---|---|
| المؤلفون: | Suárez, Pol, Alcántara-Ávila, Francisco, Miró, Arnau, Rabault, Jean, Font, Bernat, Lehmkuhl, Oriol, Vinuesa, R. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Physics - Fluid Dynamics, Computer Science - Machine Learning, 76F70, I.2.0, I.6.0 |
| الوصف: | 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. Comment: ETMM14 2023 conference proceeding paper |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2309.02462 |
| رقم الأكسشن: | edsarx.2309.02462 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |