تقرير
Active flow control for drag reduction through multi-agent reinforcement learning on a turbulent cylinder at $Re_D=3900$
| العنوان: | Active flow control for drag reduction through multi-agent reinforcement learning on a turbulent cylinder at $Re_D=3900$ |
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| المؤلفون: | Suárez, P., Álcantara-Ávila, F., Miró, A., Rabault, J., Font, B., Lehmkuhl, O., Vinuesa, R. |
| سنة النشر: | 2024 |
| المجموعة: | Physics (Other) |
| مصطلحات موضوعية: | Physics - Fluid Dynamics |
| الوصف: | This study presents novel active-flow-control (AFC) strategies aimed at achieving drag reduction for a three-dimensional cylinder immersed in a flow at a Reynolds number based on freestream velocity and cylinder diameter of (Re_D=3900). The cylinder in this subcritical flow regime has been extensively studied in the literature and is considered a classic case of turbulent flow arising from a bluff body. The strategies presented are explored through the use of deep reinforcement learning. The cylinder is equipped with 10 independent zero-net-mass-flux jet pairs, distributed on the top and bottom surfaces, which define the AFC setup. The method is based on the coupling between a computational-fluid-dynamics solver and a multi-agent reinforcement-learning (MARL) framework using the proximal-policy-optimization algorithm. Thanks to the acceleration in training facilitated by exploiting the local invariants with MARL, a drag reduction of (8\%) was achieved, with a mass cost efficiency two orders of magnitude lower than those of the existing classical controls in the literature. This development represents a significant advancement in active flow control, particularly in turbulent regimes critical to industrial applications. Comment: Under review in "Special Issue": Progress in Engineering Turbulence Modelling, Simulation and Measurements |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.17655 |
| رقم الأكسشن: | edsarx.2405.17655 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |