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arXiv 2408.14232physics.flu-dyn
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基于深度学习代理模型和强化学习的受限方柱尾流高效主动流动控制策略

Efficient Active Flow Control Strategy for Confined Square Cylinder Wake Using Deep Learning-Based Surrogate Model and Reinforcement Learning

Meng Zhang, Mustafa Z. Yousif, Minze Xu, Haifeng Zhou, Linqi Yu, HeeChang Lim

AI总结:

本研究提出DL-MBRL框架,结合深度学习代理模型与CFD交替交互,在保证控制效果的同时将训练时间减少49.2%,并解决了MFRL中非零平均升力系数的问题。

摘要:

本研究提出了一种基于深度学习模型的强化学习(DL-MBRL)方法,使用反相射流对二维(2D)方柱尾流进行主动控制。DL-MBRL框架在深度学习代理模型(DL-SM)和计算流体动力学(CFD)仿真之间交替交互,以抑制尾涡脱落,显著降低了计算成本。DL-SM结合了Transformer和多尺度增强超分辨率生成对抗网络(MS-ESRGAN),能够有效建模复杂的流动动力学,高效地模拟CFD环境。Transformer和MS-ESRGAN在二维直接数值模拟(DNS)数据上训练,与DNS结果表现出极好的一致性,验证了DL-SM的准确性。误差分析表明,每五次交互用CFD替换一次DL-SM可保持可靠性。尽管DL-MBRL在训练期间表现出不如无模型强化学习(MFRL)的鲁棒收敛性,但它将训练时间减少了49.2%,从41.87小时降至20.62小时。MFRL和DL-MBRL均实现了98%的脱落能量减少和95%的升力系数(C_L)标准差减少。然而,由于探索不足,MFRL表现出非零的平均升力系数,而DL-MBRL通过利用DL-SM的随机性改善了探索,解决了非零平均C_L的问题。本研究表明,DL-MBRL在流动稳定方面不仅与MFRL效果相当,而且更优,同时训练时间显著减少,突显了将深度强化学习与DL-SM结合以增强主动流动控制的潜力。

英文摘要:

This study presents a deep learning model-based reinforcement learning (DL-MBRL) approach for active control of two-dimensional (2D) wake flow past a square cylinder using antiphase jets. The DL-MBRL framework alternates between interacting with a deep learning surrogate model (DL-SM) and computational fluid dynamics (CFD) simulations to suppress wake vortex shedding, significantly reducing computational costs. The DL-SM, which combines a Transformer and a multiscale enhanced super-resolution generative adversarial network (MS-ESRGAN), effectively models complex flow dynamics, efficiently emulating the CFD environment. Trained on 2D direct numerical simulation (DNS) data, the Transformer and MS-ESRGAN demonstrated excellent agreement with DNS results, validating the DL-SM's accuracy. Error analysis suggests replacing the DL-SM with CFD every five interactions to maintain reliability. While DL-MBRL showed less robust convergence than model-free reinforcement learning (MFRL) during training, it reduced training time by 49.2%, from 41.87 hours to 20.62 hours. Both MFRL and DL-MBRL achieved a 98% reduction in shedding energy and a 95% reduction in the standard deviation of the lift coefficient (C_L). However, MFRL exhibited a nonzero mean lift coefficient due to insufficient exploration, whereas DL-MBRL improved exploration by leveraging the randomness of the DL-SM, resolving the nonzero mean C_L issue. This study demonstrates that DL-MBRL is not only comparably effective but also superior to MFRL in flow stabilization, with significantly reduced training time, highlighting the potential of combining deep reinforcement learning with DL-SM for enhanced active flow control.

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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