AI 中文总结
研究湍流减阻的闭环壁面控制问题，采用进化策略（ES）在大型湍流通道上直接优化循环闭环控制器，ES控制器减阻约26%，超基于梯度的多智能体控制器及经典对立控制，且与二者在缓冲层轨迹不同、驱动相关对象有别。
AI 中文摘要
通过多智能体强化学习学习的闭环壁面控制可以降低湍流通道中的皮肤摩擦阻力，但这些基于梯度的策略是在小周期盒子上训练的，在应用于更大区域时性能会降低。我们最近表明，这种策略也容易出现饱和的开关式驱动，从而坍缩成流向驻波，其尺度由计算盒子而非近壁周期设定，并提出了避免这些退化的架构修正。在此，我们采用进化策略（ES）在\(\mathit{Re}_{\tau}\simeq180\)的大型湍流通道上直接优化循环闭环控制器，使用能量感知标准在全流场情节中评估策略性能，并并行处理候选策略。据我们所知，这是进化策略在湍流控制中的首次应用。ES控制器将皮肤摩擦降低了约26%，超过了在最小盒子上训练的基于梯度的多智能体控制器Cavallazzi等人（2026年）的GRU-MARL（17%），并略微超过经典的对立控制（OC，22%）。对摩擦、雷诺应力剖面和各向异性不变量的壁面法向分解表明，ES和对立控制的流动在缓冲层中遵循不同的轨迹，通过近壁湍流的不同重组达到可比的减阻效果。特别是，ES驱动主要与流向速度波动相关，而不是与经典OC目标中的壁面法向速度相关。
英文摘要
Closed-loop wall controllers learnt by multi-agent reinforcement learning are usually trained on periodic boxes far smaller than the flows they are meant to drive, and a large part of their drag reduction is lost when they are carried across. Retraining on the target domain is not an affordable remedy: the centralised critic that assigns credit to each wall patch degrades as patches are added, the zero-net-mass constraint couples the patches it is asked to separate, and the episodes must be collected in sequence at a cost that grows with the domain. We propose instead a short gradient-free refinement stage, applied to the transferred policy on the domain it will drive. An evolution strategy scores whole flow episodes against a regularised objective, so no credit has to be assigned to individual patches, and the candidates in a generation are independent and run in parallel. Applied to a recurrent multi-agent policy trained on a minimal flow unit at $\Retau\simeq180$ and evaluated on a channel sixteen times larger in wall-parallel area, a few generations raise the drag reduction from $19.0\%$ to $25.7\%$, above the $22.5\%$ of opposition control. Because the policies before and after refinement share an architecture and a training history, the difference between the controlled flows follows from the refinement alone. It shows in the friction decomposition, in the Reynolds stresses and in the near-wall spectra, and the actuation moves from a weak coupling to the wall-normal velocity towards a strong coupling to the streamwise fluctuation.
