AI 中文总结
本研究将深度强化学习（DRL）用于单侧扩散器湍流风洞流动的主动分离控制，采用近端策略优化学习，实现了约53%的前向流动分数，优于常规开环控制，是识别鲁棒控制策略的高效工具。
AI 中文摘要
本研究将深度强化学习（DRL）作为无模型闭环主动分离控制工具，应用于单侧扩散器上方的完全湍流风洞流动场景。智能体控制一组磁阀（开/关状态）向边界层喷射压缩空气，环境状态简化为自然转捩分离点附近的单壁面切应力传感器信号。控制律通过近端策略优化（PPO）实时学习。与基于动作后所有奖励加权求和的标准学习设计相比，本研究表明，与流动对流时间对齐的时间范围可实现更快收敛和更鲁棒的控制策略。所得控制律对应低占空比驱动模式，产生的前向流动分数约为53%，优于常规和优化的周期性开环控制（分别约为40%和51%）。研究结果表明，当嵌入在线实验时，DRL是识别鲁棒且可解释的主动分离控制策略的高效工具。
英文摘要
This work investigates Deep Reinforcement Learning (DRL) as a tool for model-free closed-loop active separation control in a fully turbulent wind tunnel flow over a one-sided diffuser. The agent controls an array of magnetic valves (on/off) that eject compressed air into the boundary layer, while the environmental state is reduced to the signal from a single wall-shear-stress sensor placed near the natural transitory detachment point. The control law is learned in real time using Proximal Policy Optimization. Compared to the standard learning design based on the weighted sum of all rewards following an action, we demonstrate that a horizon aligned with the convective time of the flow leads to faster convergence and a more robust control strategy. The resulting control law corresponds to a low-duty-cycle actuation pattern that yields a forward-flow fraction of approximately $53\%$. This compares favorably with conventional and optimized periodic open-loop control ($\sim 40\%$ and $\sim 51\%$, respectively). The findings of this article indicate that, when embedded into an online experiment, DRL represents an efficient tool to identify robust and interpretable active separation control strategies.
