The real power of artificial intelligence appears in reinforcement learning, which is computationally and physically more sophisticated due to its dynamic nature. Rotation and injection are some of the proven ways in active flow control for drag reduction on blunt bodies. In this paper, rotation will be added to the cylinder alongside the deep reinforcement learning (DRL) algorithm, which uses multiple controlled jets to reach the maximum possible drag suppression. Characteristics of the DRL code, including controlling parameters, their limitations, and optimization of the DRL network for use with rotation will be presented. This work will focus on optimizing the number and positions of the jets, the sensors location, and the maximum allowed flow rate to jets in the form of the maximum allowed flow rate of each actuation and the total number of them per episode. It is found that combining the rotation and DRL is promising since it suppresses the vortex shedding, stabilizes the Karman vortex street, and reduces the drag coefficient by up to 49.75%. Also, it will be shown that having more sensors at more locations is not always a good choice and the sensor number and location should be determined based on the need of the user and corresponding configuration. Also, allowing the agent to have access to higher flow rates, mostly reduces the performance, except when the cylinder rotates. In all cases, the agent can keep the lift coefficient at a value near zero, or stabilize it at a smaller number.

翻译：人工智能的真正力量体现在强化学习中，其动态特性使其在计算和物理层面更加复杂。旋转与射流注入是钝体减阻主动流动控制中已被验证的有效方法。本文将在圆柱旋转条件下引入深度强化学习（DRL）算法，该算法通过多组可控射流实现最大可能的阻力抑制。将详细阐述DRL代码的特性，包括控制参数、参数约束条件，以及面向旋转工况的DRL网络优化方案。本研究重点优化射流数量与位置、传感器布局、单次动作最大允许流量以及每个训练回合的总射流次数。研究发现，旋转与DRL的结合具有显著优势：可抑制涡脱落、稳定卡门涡街，并使阻力系数降低高达49.75%。同时表明，并非传感器数量越多或布局越广越好，传感器配置需根据用户需求及对应构型择优确定。此外，允许智能体使用更高流量通常会降低性能，除非圆柱处于旋转状态。在所有工况下，智能体均能将升力系数维持在接近于零的水平，或将其稳定在更小数值。