A non-local spectral transfer model and new scaling law for scalar turbulence
A non-local spectral transfer model and new scaling law for scalar turbulence
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DOI：
10.1017/jfm.2022.1066
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发表时间：
2021-11
影响因子：
3.7
通讯作者：
Ali Akhavan-Safaei;Mohsen Zayernouri
中科院分区：
文献类型：
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作者：
Ali Akhavan-Safaei;Mohsen Zayernouri
摘要 在这项研究中，我们重新审视了被动标量传输（在大尺度各向异性强迫下）中湍流强度的光谱传递模型，并对标量方差级联的缩放进行了后续修改。从修改后的光谱传递模型中，我们使用分数阶拉普拉斯算子获得了修改后的标量传递模型，该模型有助于稳健地包含源自在湍流级联中跨多个尺度传递的大尺度各向异性的非局部效应。我们根据标量谱的缩放分析为非局部模型提供先验估计，然后通过直接数值模拟检查我们开发的模型。我们对标量方差的演化、标量梯度的高阶统计以及湍流传输的重要两点统计指标进行了详细分析，以对非局部模型与其标准版本进行全面比较。最后，我们提出了一种分析，当滤波器尺度接近湍流传输的耗散尺度时，无缝协调了所开发的模型与用于大涡模拟的分数阶亚网格尺度标量通量模型（Akhavan-Safaei 等人，J. Comput. Phys.，第 446 卷，2021，110571）之间的相似性。为了执行此任务，我们采用高斯过程回归模型来预测分数阶子网格模型的模型系数。
Abstract In this study, we revisit the spectral transfer model for the turbulent intensity in passive scalar transport (under large-scale anisotropic forcing), and a subsequent modification to the scaling of scalar variance cascade is presented. From the modified spectral transfer model, we obtain a revised scalar transport model using a fractional-order Laplacian operator that facilitates the robust inclusion of the non-local effects originating from large-scale anisotropy transferred across the multitude of scales in the turbulent cascade. We provide an a priori estimate for the non-local model based on the scaling analysis of the scalar spectrum, and later examine our developed model through direct numerical simulation. We present a detailed analysis on the evolution of the scalar variance, high-order statistics of the scalar gradient and important two-point statistical metrics of the turbulent transport to make a comprehensive comparison between the non-local model and its standard version. Finally, we present an analysis that seamlessly reconciles the similarities between the developed model with the fractional-order subgrid-scale scalar flux model for large-eddy simulation (Akhavan-Safaei et al., J. Comput. Phys., vol. 446, 2021, 110571) when the filter scale approaches the dissipative scales of turbulent transport. In order to perform this task, we employ a Gaussian process regression model to predict the model coefficient for the fractional-order subgrid model.