تقرير
Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning
| العنوان: | Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning |
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| المؤلفون: | Suárez, P., Álcantara-Ávila, F., Rabault, J., Miró, A., Font, B., Lehmkuhl, O., Vinuesa, R. |
| سنة النشر: | 2024 |
| المجموعة: | Physics (Other) |
| مصطلحات موضوعية: | Physics - Fluid Dynamics |
| الوصف: | Designing active-flow-control (AFC) strategies for three-dimensional (3D) bluff bodies is a challenging task with critical industrial implications. In this study we explore the potential of discovering novel control strategies for drag reduction using deep reinforcement learning. We introduce a high-dimensional AFC setup on a 3D cylinder, considering Reynolds numbers ($Re_D$) from $100$ to $400$, which is a range including the transition to 3D wake instabilities. The setup involves multiple zero-net-mass-flux jets positioned on the top and bottom surfaces, aligned into two slots. The method relies on coupling the computational-fluid-dynamics solver with a multi-agent reinforcement-learning (MARL) framework based on the proximal-policy-optimization algorithm. MARL offers several advantages: it exploits local invariance, adaptable control across geometries, facilitates transfer learning and cross-application of agents, and results in a significant training speedup. For instance, our results demonstrate $21\%$ drag reduction for $Re_D=300$, outperforming classical periodic control, which yields up to $6\%$ reduction. To the authors' knowledge, the present MARL-based framework represents the first time where training is conducted in 3D cylinders. This breakthrough paves the way for conducting AFC on progressively more complex turbulent-flow configurations. Comment: Under review in Nature Engineering Communications |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.17210 |
| رقم الأكسشن: | edsarx.2405.17210 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |