研究人员利用进化策略（ES）开发了一种用于湍流减阻的闭环壁面控制器，这是ES首次应用于湍流控制。这种无梯度方法实现了26%的皮肤摩擦阻力降低，优于之前的基于梯度的多智能体强化学习控制器和经典的对立控制。ES控制器的有效性源于其与流向速度波动的相关性，这与对立控制侧重于壁面法向速度不同。 AI
影响 这项研究展示了一种解决复杂控制问题的、新颖的无梯度方法，可能适用于其他工程领域。
排序理由 该集群描述了一种进化策略在流体动力学问题中的新颖应用，该应用发表在一篇arXiv论文中。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
arXiv:2607.12626v1 Announce Type: cross Abstract: Closed-loop wall control learnt by multi-agent reinforcement learning can lower skin-friction drag in turbulent channels, but these gradient-based policies are trained on small periodic boxes and exhibit reduced performance when c…
Closed-loop wall control learnt by multi-agent reinforcement learning can lower skin-friction drag in turbulent channels, but these gradient-based policies are trained on small periodic boxes and exhibit reduced performance when carried over to a larger domain. We recently showed…
Closed-loop wall control learnt by multi-agent reinforcement learning can lower skin-friction drag in turbulent channels, but these gradient-based policies are trained on small periodic boxes and exhibit reduced performance when carried over to a larger domain. We recently showed…