Transport dynamics of self-consistent, near-marginal drift-wave turbulence. II. Characterization of transport by means of passive scalars

From theoretical and modeling points of view, following Lagrangian trajectories is the most straightforward way to characterize the transport dynamics. In real plasmas, following Lagrangian trajectories is difficult or impossible. Using a blob of passive scalar (a tracer blob) allows a quasi-Lagrang...

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Bibliographic Details
Published in:Physics of Plasmas
Main Authors: Ogata, D., Newman, D.E., Sánchez Fernández, Luis Raúl
Other Authors: Ministerio de Ciencia e Innovación (España)
Format: Article in Journal/Newspaper
Language:English
Published: AIP Publishing 2017
Subjects:
Online Access:http://hdl.handle.net/10016/35716
https://doi.org/10.1063/1.4993211
Description
Summary:From theoretical and modeling points of view, following Lagrangian trajectories is the most straightforward way to characterize the transport dynamics. In real plasmas, following Lagrangian trajectories is difficult or impossible. Using a blob of passive scalar (a tracer blob) allows a quasi-Lagrangian view of the dynamics. Using a simple two-dimensional electrostatic plasma turbulence model, this work demonstrates that the evolution of the tracers and the passive scalar field is equivalent between these two fluid transport viewpoints. When both the tracers and the passive scalar evolve in tandem and closely resemble stable distributions, namely, Gaussian distributions, the underlying turbulent transport character can be recovered from the temporal scaling of the second moments of both. This local transport approach corroborates the use of passive scalar as a turbulent transport measurement. The correspondence between the local transport character and the underlying transport is quantified for different transport regimes ranging from subdiffusive to superdiffusive. This correspondence is limited to the initial time periods of the spread of both the tracers and the passive scalar in the given transport regimes. This work was supported by U.S. DOE Contract No. DE-FG02-04ER54741 with the University of Alaska Fairbanks and in part by a grant of HPC resources from the Arctic Region Supercomputing Center at the University of Alaska Fairbanks. This research was also sponsored in part by DGICYT (Dirección General de Investigaciones Científicas y Tecnológicas) of Spain under Project No. ENE2015-68265.