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arXiv 1807.03239physics.flu-dyn
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螺旋度动力学、流体中的反向和双向叶栅以及MHD turbulence:简要评述

Helicity dynamics, inverse and bi-directional cascades in fluid and MHD turbulence: A brief review

A. Pouquet, D. Rosenberg, J. E. Stawarz, R. Marino

摘要:

我们简要回顾了螺旋度动力学,反向和双向级联流体和磁流体动力学(MHD)湍流,重点是后者。湍流系统的能量,在非耗散情况下的不变量,通过非线性模式耦合转移到小尺度。五十年前,人们认识到,对于二维流体,能量级联而不是更大的尺度,三维MHD中的磁螺旋度也是如此。然而,最近获得的证据表明,事实上,对于一系列的控制参数,有系统,其理想的不变量可以转移,恒定的通量,大尺度和小尺度,如MHD或旋转分层流,在后一种情况下,包括准地转强迫。这种双向、分裂、级联直接影响在这些流中发生混合和耗散的速率,其中非线性涡流与具有各向异性色散定律的快波相互作用,例如由于施加的旋转、分层或均匀磁场。级联的方向可以在某些情况下,通过使用唯象参数,其中之一,我们在这里得出以下的情况下,在电子MHD的逆磁螺旋级联的经典线路。随着来自大型实验室实验、高性能计算和现场卫星观测的更高分辨率的数据集,机器学习工具正在为湍流研究带来新的视角,例如帮助设计新的显式次网格尺度参数化,这可能导致增强物理洞察力,包括在未来这些新的双向级联的情况下。

英文摘要:

We briefly review helicity dynamics, inverse and bi-directional cascades in fluid and magnetohydrodynamic (MHD) turbulence, with an emphasis on the latter. The energy of a turbulent system, an invariant in the non-dissipative case, is transferred to small scales through nonlinear mode coupling. Fifty years ago, it was realized that, for a two-dimensional fluid, energy cascades instead to larger scales, and so does magnetic helicity in three-dimensional MHD. However, evidence obtained recently indicates that in fact, for a range of governing parameters, there are systems for which their ideal invariants can be transferred, with constant fluxes, to both the large scales and the small scales, as for MHD or rotating stratified flows, in the latter case including with quasi-geostrophic forcing. Such bi-directional, split, cascades directly affect the rate at which mixing and dissipation occur in these flows in which nonlinear eddies interact with fast waves with anisotropic dispersion laws, due for example to imposed rotation, stratification or uniform magnetic fields. The directions of cascades can be obtained in some cases through the use of phenomenological arguments, one of which we derive here following classical lines in the case of the inverse magnetic helicity cascade in electron MHD. With more highly-resolved data sets stemming from large laboratory experiments, high-performance computing and in-situ satellite observations, machine-learning tools are bringing novel perspectives to turbulence research, e.g. in helping devise new explicit sub-grid scale parameterizations, which may lead to enhanced physical insight, including in the future in the case of these new bi-directional cascades.

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