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通过希尔伯特-黄变换的拉格朗日单粒子湍流统计

Lagrangian single-particle turbulent statistics through the Hilbert-Huang transform.

作者信息

Huang Yongxiang, Biferale Luca, Calzavarini Enrico, Sun Chao, Toschi Federico

机构信息

Shanghai Institute of Applied Mathematics and Mechanics, Shanghai Key Laboratory of Mechanics in Energy Engineering, Shanghai University, Shanghai 200072, People's Republic of China.

出版信息

Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Apr;87(4):041003. doi: 10.1103/PhysRevE.87.041003. Epub 2013 Apr 22.

Abstract

The Hilbert-Huang transform is applied to analyze single-particle Lagrangian velocity data from numerical simulations of hydrodynamic turbulence. The velocity trajectory is described in terms of a set of intrinsic mode functions C(i)(t) and of their instantaneous frequency ω(i)(t). On the basis of this decomposition we define the ω-conditioned statistical moments of the C(i) modes, named q-order Hilbert spectra (HS). We show that such quantities have enhanced scaling properties as compared to traditional Fourier transform- or correlation-based (structure functions) statistical indicators, thus providing better insights into the turbulent energy transfer process. We present clear empirical evidence that the energylike quantity, i.e., the second-order HS, displays a linear scaling in time in the inertial range, as expected from a dimensional analysis. We also measure high-order moment scaling exponents in a direct way, without resorting to the extended self-similarity procedure. This leads to an estimate of the Lagrangian structure function exponents which are consistent with the multifractal prediction in the Lagrangian frame as proposed by Biferale et al. [Phys. Rev. Lett. 93, 064502 (2004)].

摘要

希尔伯特-黄变换被应用于分析来自流体动力学湍流数值模拟的单粒子拉格朗日速度数据。速度轨迹是根据一组固有模态函数C(i)(t)及其瞬时频率ω(i)(t)来描述的。基于这种分解,我们定义了C(i)模式的ω条件统计矩,称为q阶希尔伯特谱(HS)。我们表明,与传统的基于傅里叶变换或相关性(结构函数)的统计指标相比,这些量具有增强的标度特性,从而能更好地洞察湍流能量传递过程。我们给出了明确的经验证据,即类能量量,也就是二阶HS,在惯性范围内随时间呈线性标度,这与量纲分析的预期一致。我们还直接测量了高阶矩标度指数,而无需借助扩展自相似性程序。这导致了对拉格朗日结构函数指数的估计,该估计与比费拉莱等人[《物理评论快报》93, 064502 (2004)]提出的拉格朗日框架中的多重分形预测一致。

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