Helicity Dynamics, Inverse, and Bidirectional Cascades in Fluid and Magnetohydrodynamic Turbulence: A Brief Review. Issue 3 (7th March 2019)

• Record Type: Journal Article
• Authors: Pouquet, A.; Rosenberg, D.; Stawarz, J.E.; Marino, R.
• Is Part Of: Earth and space science. Volume 6:Issue 3(2019)
• Page Start: 351
• Page End: 369
• Publication Date: 2019-03-07
• Subjects: Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2018EA000432
• Languages: English
• ISSNs: 2333-5084

Abstract

Abstract : 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. Plain Language Summary: Turbulent flows are prevalent in Geophysics and Space Physics. They are complex and involve interactions between fluctuations at widely separated scales, with the energy expected in the general case to flow only to small scales where it is dissipated. It was found recently that, contrary to such expectations, energy can go in substantial amounts to both the small and large scales, in the presence of magnetic fields, as applicable to space plasmas, and for rotating stratified flows as encountered in the atmosphere and the oceans. This result implies that the amount of energy available for dissipation may differ from flow to flow, and simple scaling arguments allow for predictions that are backed up by results stemming from direct numerical simulations. One should incorporate this bidirectional cascade phenomenon in the turbulence models used for global computations of geophysical and astrophysical media. Furthermore, machine learning tools may prove useful in deriving such enhanced models in their capacity to interrogate the large data bases that already exist for such complex flows. Key Points: Magnetic helicity displays an inverse cascade to large scales which, in electron MHD, can be justified with a simple phenomenological model Total energy in MHD or rotating stratified turbulence has constant‐flux cascades to small and large scales, affecting mixing and dissipation With these results, needed modifications to subgrid scale turbulence models will be enhanced using tools from big data and machine learning