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arXiv 2407.15604physics.flu-dyn
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使用广义零样本学习高灵活性重建壁面湍流中的小尺度运动

High-flexibility reconstruction of small-scale motions in wall turbulence using a generalized zero-shot learning

Haokai Wu, Kai Zhang, Dai Zhou, Wen-Li Chen, Zhaolong Han, Yong Cao

AI总结:

本研究提出EC-SRGAN超分辨率框架,结合SRGAN与下采样模块并引入连续性方程残差,通过零样本迁移高效、灵活地重建多倍率壁面湍流边界层小尺度运动。

摘要:

本研究提出了一种新颖的超分辨率(或SR)框架,用于从低分辨率输入生成高分辨率湍流边界层(TBL)流场。该框架将超分辨率生成对抗神经网络(SRGAN)与下采样模块(DMs)相结合,并将连续性方程的残差整合到损失函数中。在超分辨率处理之前,DMs选择性地滤除低分辨率场中能量耗散过大的成分。该框架迭代应用SRGAN和DM过程,以充分捕捉多尺度流场结构的能量级联,统称为基于SRGAN的能量级联框架(EC-SRGAN)。尽管仅在湍流通道流数据上进行训练(通过“零样本迁移”),EC-SRGAN在预测TBL小尺度速度场方面展现出卓越的泛化能力,与DNS结果相比,能准确再现波数谱。此外,在特定的超分辨率倍率下训练了一个超分辨率核心。通过利用该预训练超分辨率核心,EC-SRGAN能从不同级别的低分辨率输入中高效重建多个超分辨率倍率的TBL场,展现出强大的灵活性。通过学习湍流尺度不变性,EC-SRGAN在不同TBL数据集上表现出鲁棒性。这些结果突显了EC-SRGAN在以高灵活性生成和预测壁面湍流方面的潜力,为应对各种TBL相关挑战提供了广阔的应用前景。

英文摘要:

This study proposes a novel super-resolution (or SR) framework for generating high-resolution turbulent boundary layer (TBL) flow from low-resolution inputs. The framework combines a super-resolution generative adversarial neural network (SRGAN) with down-sampling modules (DMs), integrating the residual of the continuity equation into the loss function. DMs selectively filter out components with excessive energy dissipation in low-resolution fields prior to the super-resolution process. The framework iteratively applies the SRGAN and DM procedure to fully capture the energy cascade of multi-scale flow structures, collectively termed the SRGAN-based energy cascade framework (EC-SRGAN). Despite being trained solely on turbulent channel flow data (via "zero-shot transfer"), EC-SRGAN exhibits remarkable generalization in predicting TBL small-scale velocity fields, accurately reproducing wavenumber spectra compared to DNS results. Furthermore, a super-resolution core is trained at a specific super-resolution ratio. By leveraging this pre-trained super-resolution core, EC-SRGAN efficiently reconstructs TBL fields at multiple super-resolution ratios from various levels of low-resolution inputs, showcasing strong flexibility. By learning turbulent scale invariance, EC-SRGAN demonstrates robustness across different TBL datasets. These results underscore EC-SRGAN potential for generating and predicting wall turbulence with high flexibility, offering promising applications in addressing diverse TBL-related challenges.

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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