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arXiv 2301.09889physics.flu-dyn
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深度强化学习在槽道湍流减阻中的应用

Deep reinforcement learning for turbulent drag reduction in channel flows

L. Guastoni, J. Rabault, P. Schlatter, H. Azizpour, R. Vinuesa

摘要:

我们引入了一个强化学习(RL)的环境来设计和基准控制策略,旨在减少湍流流体流动中的阻力封闭在一个通道。该环境提供了一个计算效率高、并行化、高保真流体模拟的框架,可以与已建立的RL代理编程接口进行交互。这既可以测试现有的深度强化学习(DRL)算法来应对具有挑战性的任务,也可以推进我们对复杂、动荡的物理系统的了解,这是两个多世纪以来的一个主要研究课题,即使在今天,仍然是许多未回答问题的主题。该控制以吹吸的形式应用于墙壁,而可观察的状态是可配置的,允许在域的不同位置选择不同的变量,如速度和压力。考虑到湍流复杂的非线性特性,迄今为止在文献中提出的控制策略是物理上有根据的,但过于简单。相比之下,DRL能够利用可以从流动模拟中采样的高维数据来设计高级控制策略。为了建立一个测试数据驱动控制策略的基准,我们比较了反对控制,一个国家的最先进的服从控制策略,从文献中,和一个常用的DRL算法,深确定性政策梯度。我们的研究结果表明,DRL导致43%和46%的阻力减少在一个最小的和一个较大的通道(在摩擦雷诺数为180),分别优于经典的反对派控制约20个百分点。

英文摘要:

We introduce a reinforcement learning (RL) environment to design and benchmark control strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The environment provides a framework for computationally-efficient, parallelized, high-fidelity fluid simulations, ready to interface with established RL agent programming interfaces. This allows for both testing existing deep reinforcement learning (DRL) algorithms against a challenging task, and advancing our knowledge of a complex, turbulent physical system that has been a major topic of research for over two centuries, and remains, even today, the subject of many unanswered questions. The control is applied in the form of blowing and suction at the wall, while the observable state is configurable, allowing to choose different variables such as velocity and pressure, in different locations of the domain. Given the complex nonlinear nature of turbulent flows, the control strategies proposed so far in the literature are physically grounded, but too simple. DRL, by contrast, enables leveraging the high-dimensional data that can be sampled from flow simulations to design advanced control strategies. In an effort to establish a benchmark for testing data-driven control strategies, we compare opposition control, a state-of-the-art turbulence-control strategy from the literature, and a commonly-used DRL algorithm, deep deterministic policy gradient. Our results show that DRL leads to 43% and 46% drag reduction in a minimal and a larger channel (at a friction Reynolds number of 180), respectively, outperforming the classical opposition control by around 20 percentage points.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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