流体力学和AI交叉领域的研究者值得关注——该工作用可解释AI找到了湍流减阻的节能控制策略，比传统方法效率提升近50%，且执行成本降低一个数量级。
该研究提出结合多智能体深度强化学习（MARL）与可解释深度学习（XDL）的方法，用于减少壁面湍流中的阻力。通过SHAP归因分析，比较了三种奖励设计策略，其中基于摩擦系数和壁面压力波动组合的SHAP策略表现最佳，实现了34.44%的减阻率和34.01%的净节能率，且归一化输入功率仅0.43%。相比传统对抗控制，减阻和净节能分别提升49.41%和48.52%，同时将归一化执行成本从5.90%降至0.43%。分析表明，节能策略与压力门控执行一致，主要在壁面压力接近零时激活，时间尺度与近壁湍流结构寿命相当。
Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction
We propose a method combining Multi-Agent Deep Reinforcement Learning (MARL) and eXplainable Deep Learning (XDL) to reduce drag in wall-bounded turbulent flows. Taking as a baseline the results of training agents directly targeting wall-shear stress and opposition control, three SHAP-guided approaches are compared. In the first, the reward is computed from SHAP attributions of a U-net predicting the future velocity field; in the second, from SHAP attributions of a U-net predicting the skin-friction coefficient; in the third, from a combination of SHAP attributions of two U-nets predicting the skin-friction coefficient and the wall pressure fluctuations, respectively. The combined SHAP strategy based on skin-friction coefficient and wall-pressure fluctuations achieves the best overall performance, achieving a DR of 34.44% and a NES of 34.01% with only 0.43% normalized input power. Relative to opposition control, drag reduction and net energy saving increase by 49.41% and 48.52%, respectively. Compared with the direct wall-shear-stress baseline, the proposed strategy simultaneously improves performance while reducing the normalized actuation cost from 5.90% to 0.43%. Analysis of the results reveals that the energetically efficient policy is consistent with pressure-gated actuation, activating predominantly at near-zero wall pressure, and operates on a temporal timescale comparable to the lifetime of the near-wall turbulent structures.