用物理约束神经算子实现湍流减阻，高效预测控制
Physics-informed Neural-operator Predictive Control for Drag Reduction in Turbulent Flows
- 结合物理信息神经算子与模型化强化学习，联合学习控制策略
- 在雷诺数1.5万时实现39%减阻，优于以往方法超32%
- 适合高雷诺数、未见工况的湍流控制研究者
数值评估壁面摩擦湍流控制效果面临巨大挑战，因需昂贵的湍流模拟。本文提出一种高效的深度强化学习框架，用于湍流建模与控制。该方法为基于模型的预测控制（PC），通过物理信息神经算子（PINO）联合学习控制策略与观测模型，具备网格无关性，可准确捕捉湍流细尺度特征。PINO-PC在高雷诺数且训练中未见的复杂场景下表现优异，于主流速度雷诺数15,000时实现39.0%的减阻率，优于以往流体控制方法超过32%。
原文摘要 · Abstract (English)
Assessing turbulence control effects for wall friction numerically is a significant challenge since it requires expensive simulations of turbulent fluid dynamics. We instead propose an efficient deep reinforcement learning (RL) framework for modeling and control of turbulent flows. It is model-based RL for predictive control (PC), where both the policy and the observer models for turbulence control are learned jointly using Physics Informed Neural Operators (PINO), which are discretization invariant and can capture fine scales in turbulent flows accurately. Our PINO-PC outperforms prior model-free reinforcement learning methods in various challenging scenarios where the flows are of high Reynolds numbers and unseen, i.e., not provided during model training. We find that PINO-PC achieves a drag reduction of 39.0\% under a bulk-velocity Reynolds number of 15,000, outperforming previous fluid control methods by more than 32\%.
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