论文标题
强大的流量控制和最佳的传感器放置使用深度加固学习
Robust flow control and optimal sensor placement using deep reinforcement learning
论文作者
论文摘要
本文着重于在圆柱体上方的2D模拟层流流上进行拖动控制策略。已经实施了深厚的增强学习算法来发现有效的控制方案，使用圆柱杆上的两个合成喷气机作为执行器，并在气缸后作为反馈观察后的压力传感器。目前的工作着重于确定的控制策略的效率和鲁棒性，并引入了一种新型算法（S-PPO-CMA）以优化传感器布局。在雷诺数字120时，一种节能控制策略将阻力减少了18.4％。该控制策略显示出[100,216]的雷诺数和测量噪声，持久信号与噪声比低至0.2，对性能的影响可忽略不计。除了对传感器编号和位置的系统研究外，提议的寻求稀疏算法还成功地优化了降低的5传感器布局，同时保持最先进的性能。这些结果强调了积极流动控制的强化学习的有趣可能性，并为这些控制技术在实验或工业系统中的有效，健壮和实际实现铺平了道路。
This paper focuses on a drag-reducing control strategy on a 2D-simulated laminar flow past a cylinder. Deep reinforcement learning algorithms have been implemented to discover efficient control schemes, using two synthetic jets located on the cylinder's poles as actuators and pressure sensors in the wake of the cylinder as feedback observation. The present work focuses on the efficiency and robustness of the identified control strategy and introduces a novel algorithm (S-PPO-CMA) to optimise the sensor layout. An energy-efficient control strategy reducing drag by 18.4% at Reynolds number 120 is obtained. This control policy is shown to be robust both to the Reynolds number in the range [100,216] and to measurement noise, enduring signal to noise ratios as low as 0.2 with negligible impact on performance. Along with a systematic study on sensor number and location, the proposed sparsity-seeking algorithm has achieved a successful optimisation to a reduced 5-sensor layout while keeping state-of-the-art performance. These results highlight the interesting possibilities of reinforcement learning for active flow control and pave the way to efficient, robust and practical implementations of these control techniques in experimental or industrial systems.
