Point-vortex simulation of the inverse energy cascade in two-dimensional turbulence

1981 ◽  
Vol 24 (1) ◽  
pp. 171 ◽  
Author(s):  
Eric D. Siggia
Keyword(s):  
Point Vortex ◽  
Energy Cascade ◽  
Two Dimensional ◽  
Inverse Energy Cascade

Infrared Reynolds number dependency of the two-dimensional inverse energy cascade

2011 ◽  
Vol 667 ◽  
pp. 463-473 ◽  
Author(s):  
ANDREAS VALLGREN
Keyword(s):  
Reynolds Number ◽  
Cascade Range ◽  
Energy Cascade ◽  
Energy Spectra ◽  
Small Scale ◽  
Two Dimensional ◽  
Inverse Energy Cascade ◽  
Linear Friction ◽  
Non Local ◽  
Reynolds Number Dependency

High-resolution simulations of forced two-dimensional turbulence reveal that the inverse cascade range is sensitive to an infrared Reynolds number, Reα = kf/kα, where kf is the forcing wavenumber and kα is a frictional wavenumber based on linear friction. In the limit of high Reα, the classic k−5/3 scaling is lost and we obtain steeper energy spectra. The sensitivity is traced to the formation of vortices in the inverse energy cascade range. Thus, it is hypothesized that the dual limit Reα → ∞ and Reν = kd/kf → ∞, where kd is the small-scale dissipation wavenumber, will lead to a steeper energy spectrum than k−5/3 in the inverse energy cascade range. It is also found that the inverse energy cascade is maintained by non-local triad interactions.

scholarly journals Inverse Energy Cascade in Forced Two-Dimensional Quantum Turbulence

2013 ◽  
Vol 110 (10) ◽  
Author(s):  
Matthew T. Reeves ◽  
Thomas P. Billam ◽  
Brian P. Anderson ◽  
Ashton S. Bradley
Keyword(s):  
Quantum Turbulence ◽  
Energy Cascade ◽  
Two Dimensional ◽  
Inverse Energy Cascade

Charney isotropy and equipartition in quasi-geostrophic turbulence

2010 ◽  
Vol 656 ◽  
pp. 448-457 ◽  
Author(s):  
ANDREAS VALLGREN ◽  
ERIK LINDBORG
Keyword(s):  
High Resolution ◽  
Kinetic Energy ◽  
Horizontal Velocity ◽  
Energy Cascade ◽  
Two Dimensional ◽  
Inverse Energy Cascade ◽  
Wide Range ◽  
Geostrophic Turbulence ◽  
Decaying Turbulence ◽  
Enstrophy Cascade

High-resolution simulations of forced quasi-geostrophic (QG) turbulence reveal that Charney isotropy develops under a wide range of conditions, and constitutes a preferred state also in β-plane and freely decaying turbulence. There is a clear analogy between two-dimensional and QG turbulence, with a direct enstrophy cascade that is governed by the prediction of Kraichnan (J. Fluid Mech., vol. 47, 1971, p. 525) and an inverse energy cascade following the classic k−5/3 scaling. Furthermore, we find that Charney's prediction of equipartition between the potential and kinetic energy in each of the two horizontal velocity components is approximately fulfilled in the inertial ranges.

scholarly journals STRUCTURE FORMATION IN SPECTRALLY CONDENSED TURBULENCE

2012 ◽  
Vol 19 ◽  
pp. 257-261
Author(s):  
HUA XIA ◽  
MICHAEL SHATS
Keyword(s):  
Structure Formation ◽  
Laboratory Experiments ◽  
Energy Cascade ◽  
Experimental Results ◽  
Two Dimensional ◽  
Radial Transport ◽  
Structural Formation ◽  
2D Turbulence ◽  
Inverse Energy Cascade

Two-dimensional (2D) turbulence supports the inverse energy cascade and the spectral condensate generation, which are studied in laboratory experiments. The generation of the spectral condensation via the inverse energy cascade dramatically reduces the radial transport in 2D flows. In this paper we report experimental results related to the formation of spectral condensates. The dynamics of the structural formation is reposted.

scholarly journals Robustness of vortex populations in the two-dimensional inverse energy cascade

2018 ◽  
Vol 850 ◽  
pp. 844-874
Author(s):  
B. H. Burgess ◽  
R. K. Scott
Keyword(s):  
Vortex Formation ◽  
Energy Cascade ◽  
Scaling Theory ◽  
Small Scale ◽  
Two Dimensional ◽  
Number Density ◽  
Scale Invariant ◽  
Inverse Energy Cascade ◽  
Simulation Parameters ◽  
The Impact

We study how the properties of forcing and dissipation affect the scaling behaviour of the vortex population in the two-dimensional turbulent inverse energy cascade. When the flow is forced at scales intermediate between the domain and dissipation scales, the growth rates of the largest vortex area and the spectral peak length scale are robust to all simulation parameters. For white-in-time forcing the number density distribution of vortex areas follows the scaling theory predictions of Burgess & Scott (J. Fluid Mech., vol. 811, 2017, pp. 742–756) and shows little sensitivity either to the forcing bandwidth or to the nature of the small-scale dissipation: both narrowband and broadband forcing generate nearly identical vortex populations, as do Laplacian diffusion and hyperdiffusion. The greatest differences arise in comparing simulations with correlated forcing to those with white-in-time forcing: in flows with correlated forcing the intermediate range in the vortex number density steepens significantly past the predicted scale-invariant $A^{-1}$ scaling. We also study the impact of the forcing Reynolds number $Re_{f}$, a measure of the relative importance of nonlinear terms and dissipation at the forcing scale, on vortex formation and the scaling of the number density. As $Re_{f}$ decreases, the flow changes from one dominated by intense circular vortices surrounded by filaments to a less structured flow in which vortex formation becomes progressively more suppressed and the filamentary nature of the surrounding vorticity field is lost. However, even at very small $Re_{f}$, and in the absence of intense coherent vortex formation, regions of anomalously high vorticity merge and grow in area as predicted by the scaling theory, generating a three-part number density similar to that found at higher $Re_{f}$. At late enough stages the aggregation process results in the formation of long-lived circular vortices, demonstrating a strong tendency to vortex formation, and via a route distinct from the axisymmetrization of forcing extrema seen at higher $Re_{f}$. Our results establish coherent vortices as a robust feature of the two-dimensional inverse energy cascade, and provide clues as to the dynamical mechanisms shaping their statistics.

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