Helicity Dynamics, Inverse, and Bidirectional Cascades in Fluid and Magnetohydrodynamic Turbulence: A Brief Review
Helicity Dynamics, Inverse, and Bidirectional Cascades in Fluid and Magnetohydrodynamic Turbulence: A Brief Review
复制标题
流体和磁流体动力湍流中的螺旋动力学、逆向和双向级联：简要回顾
作者：
A. Pouquet;D. Rosenberg;J. Stawarz;R. Marino
我们简要回顾了流体和磁流体动力学（MHD）湍流中的螺旋动力学、逆级联和双向级联，重点介绍了后者。湍流系统的能量在非耗散情况下是不变量，它通过非线性模态耦合传递到小尺度上。50年前，人们认识到，对于二维流体，能量级联到更大的尺度，MHD中的磁激发也是如此。然而，最近获得的证据表明，事实上，对于一系列控制参数，存在一些系统，其理想不变量可以以恒定的通量转移到大尺度和小尺度，如MHD或旋转分层流，在后者的情况下包括准地转强迫。在这些流动中，非线性涡流与具有各向异性色散规律的快波相互作用，例如，由于施加的旋转、分层或均匀磁场，这种双向、分裂、级联直接影响混合和耗散发生的速率。在某些情况下，级联的方向可以通过使用现象学论证得到，其中一个我们在这里推导了电子MHD中逆磁螺旋级联的经典线。随着来自大型实验室实验、高性能计算和原位卫星观测的高分辨率数据集的出现，机器学习工具为湍流研究带来了新的视角。这样的算法有助于设计新的明确的子网格尺度参数化，这反过来可能导致增强的物理洞察力，包括在未来这些新的双向级联的情况下。
We briefly review helicity dynamics, inverse and bidirectional cascades in fluid and magnetohydrodynamic (MHD) turbulence, with an emphasis on the latter. The energy of a turbulent system, an invariant in the nondissipative 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 excitation in 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 quasi‐geostrophic forcing. Such bidirectional, 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. Such algorithms help devise new explicit subgrid‐scale parameterizations, which in turn may lead to enhanced physical insight, including in the future in the case of these new bidirectional cascades.
影响因子：
5.2
作者：
M. Azpiroz-Zabala;M. Cartigny;E. Sumner;M. Clare;P. Talling;D. Parsons;C. Cooper
通讯作者：
M. Azpiroz-Zabala;M. Cartigny;E. Sumner;M. Clare;P. Talling;D. Parsons;C. Cooper
微型游泳器悬浮液中介尺度动力学的流体动力学理论的推导
DOI：
10.1103/physreve.97.022613
发表时间：
2018
期刊：
Physical review. E
影响因子：
--
作者：
H. Reinken;S. H. L. Klapp;M. Bär;S. Heidenreich
通讯作者：
S. Heidenreich
影响因子：
3.1
作者：
Baerenzung, Julien;Rosenberg, D.;Pouquet, A.
通讯作者：
Pouquet, A.