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arXiv 2311.06112physics.flu-dyncs.LGnlin.CDphysics.ao-ph

基于深度学习扩散生成模型的湍流标度律

Turbulence Scaling from Deep Learning Diffusion Generative Models

Université du Québec à Montréal(魁北克大学蒙特利尔分校);Jagiellonian University(雅盖隆大学);Tel-Aviv University(特拉维夫大学)

Tim Whittaker, Romuald A. Janik, Yaron Oz

AI总结:

该研究利用扩散生成模型学习二维湍流涡度分布并生成新湍流解,验证其统计标度特性符合Kolmogorov标度,证明模型可捕捉真实湍流核心特征。

摘要:

复杂时空结构是湍流流动的固有特征,理解这类结构是一项重大挑战,而这一认知过程需要先掌握湍流流动构型的分布空间。我们采用基于扩散的生成模型学习湍流涡度剖面的分布,生成不可压缩Navier-Stokes方程的湍流解快照。我们针对二维空间中的逆级联过程开展研究,生成了有别于训练数据集的多样化湍流解。我们分析了这些新生成湍流剖面的统计标度特性,计算了其结构函数、能量功率谱、速度概率分布函数以及局部能量耗散矩。所有学习得到的标度指数均与预期的Kolmogorov标度一致,这种与已确立湍流特性的吻合有力证明了该模型具备捕捉真实湍流核心特征的能力。

英文摘要:

Complex spatial and temporal structures are inherent characteristics of turbulent fluid flows and comprehending them poses a major challenge. This comprehesion necessitates an understanding of the space of turbulent fluid flow configurations. We employ a diffusion-based generative model to learn the distribution of turbulent vorticity profiles and generate snapshots of turbulent solutions to the incompressible Navier-Stokes equations. We consider the inverse cascade in two spatial dimensions and generate diverse turbulent solutions that differ from those in the training dataset. We analyze the statistical scaling properties of the new turbulent profiles, calculate their structure functions, energy power spectrum, velocity probability distribution function and moments of local energy dissipation. All the learnt scaling exponents are consistent with the expected Kolmogorov scaling. This agreement with established turbulence characteristics provides strong evidence of the model's capability to capture essential features of real-world turbulence.

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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