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Comprehensive multifractal analysis of turbulent velocity using the wavelet leaders

机译:小波前导对湍流速度的综合多重分形分析

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The multifractal framework relates the scaling properties of turbulence to its local regularity properties through a statistical description as a collection of local singularities. The multifractal properties are moreover linked to the multiplicative cascade process that creates the peculiar properties of turbulence such as intermittency. A comprehensive estimation of the multifractal properties of turbulence from data analysis, using a tool valid for all kind of singularities (including oscillating singularities) and mathematically well-founded, is thus of first importance in order to extract a reliable information on the underlying physical processes. The wavelet leaders yield a new multifractal formalism which meets all these requests. This paper aims at describing it and at applying it to experimental turbulent velocity data. After a detailed discussion of the practical use of the wavelet leader based multifractal formalism, the following questions are carefully investigated: (1) What is the dependence of multifractal properties on the Reynolds number? (2) Are oscillating singularities present in turbulent velocity data? (3) Which multifractal model does correctly account for the observed multifractal properties? Results from several data set analysis are used to discuss the dependence of the computed multifractal properties on the Reynolds number but also to assess their common or universal component. An exact though partial answer (no oscillating singularities are detected) to the issue of the presence of oscillating singularities is provided for the first time. Eventually an accurate parameterization with cumulant exponents up to order 4 confirms that the log-normal model (with c(2) = -0.025 +/- 0.002) correctly accounts for the universal multifractal properties of turbulent velocity.
机译:多重分形框架通过统计描述(作为局部奇异点的集合)将湍流的缩放特性与其局部正则性联系起来。此外,多重分形特性与乘法级联过程相关联,该级联过程会产生湍流的特殊特性,例如间歇性。因此,使用一种对各种奇异点(包括振荡奇异点)有效且在数学上有充分根据的工具,从数据分析中全面评估湍流的多重分形特性,对于提取有关基础物理过程的可靠信息至关重要。 。小波领导者提出了一种新的多重分形形式,可以满足所有这些要求。本文旨在对其进行描述并将其应用于实验湍流数据。在详细讨论了基于小波前导的多重分形形式主义的实际使用之后,仔细研究了以下问题:(1)多重分形性质对雷诺数的依赖性是什么? (2)湍流速度数据中是否存在振荡奇点? (3)哪个多重分形模型正确解释了观察到的多重分形特性?来自多个数据集分析的结果用于讨论所计算的多重分形性质对雷诺数的依赖性,还用于评估其共同或通用分量。首次提供了关于存在振动奇异点的精确但部分的答案(未检测到振动奇异点)。最终,累积量指数高达4的精确参数化确认对数正态模型(c(2)= -0.025 +/- 0.002)正确地说明了湍流速度的通用多重分形特性。

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