Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-08T05:06:00.785Z Has data issue: false hasContentIssue false

Scaling theory for vortices in the two-dimensional inverse energy cascade

Published online by Cambridge University Press:  16 December 2016

B. H. Burgess*
Affiliation:
School of Mathematics and Statistics, University of St Andrews, St Andrews KY16 9SS, UK
R. K. Scott
Affiliation:
School of Mathematics and Statistics, University of St Andrews, St Andrews KY16 9SS, UK
*
Email address for correspondence: [email protected]

Abstract

We propose a new similarity theory for the two-dimensional inverse energy cascade and the coherent vortex population it contains when forced at intermediate scales. Similarity arguments taking into account enstrophy conservation and a prescribed constant energy injection rate such that $E\sim t$ yield three length scales, $l_{\unicode[STIX]{x1D714}}$, $l_{E}$ and $l_{\unicode[STIX]{x1D713}}$, associated with the vorticity field, energy peak and streamfunction, and predictions for their temporal evolutions, $t^{1/2}$, $t$ and $t^{3/2}$, respectively. We thus predict that vortex areas grow linearly in time, $A\sim l_{\unicode[STIX]{x1D714}}^{2}\sim t$, while the spectral peak wavenumber $k_{E}\equiv 2\unicode[STIX]{x03C0}l_{E}^{-1}\sim t^{-1}$. We construct a theoretical framework involving a three-part, time-evolving vortex number density distribution, $n(A)\sim t^{\unicode[STIX]{x1D6FC}_{i}}A^{-r_{i}},~i\in 1,2,3$. Just above the forcing scale ($i=1$) there is a forcing-equilibrated scaling range in which the number of vortices at fixed $A$ is constant and vortex ‘self-energy’ $E_{v}^{cm}=(2{\mathcal{D}})^{-1}\int \overline{\unicode[STIX]{x1D714}_{v}^{2}}A^{2}n(A)\,\text{d}A$ is conserved in $A$-space intervals $[\unicode[STIX]{x1D707}A_{0}(t),A_{0}(t)]$ comoving with the growth in vortex area, $A_{0}(t)\sim t$. In this range, $\unicode[STIX]{x1D6FC}_{1}=0$ and $n(A)\sim A^{-3}$. At intermediate scales ($i=2$) sufficiently far from the forcing and the largest vortex, there is a range with a scale-invariant vortex size distribution. We predict that in this range the vortex enstrophy $Z_{v}^{cm}=(2{\mathcal{D}})^{-1}\int \overline{\unicode[STIX]{x1D714}_{v}^{2}}An(A)\,\text{d}A$ is conserved and $n(A)\sim t^{-1}A^{-1}$. The final range ($i=3$), which extends over the largest vortex-containing scales, conserves $\unicode[STIX]{x1D70E}_{v}^{cm}=(2{\mathcal{D}})^{-1}\int \overline{\unicode[STIX]{x1D714}_{v}^{2}}n(A)\,\text{d}A$. If $\overline{\unicode[STIX]{x1D714}_{v}^{2}}$ is constant in time, this is equivalent to conservation of vortex number $N_{v}^{cm}=\int n(A)\,\text{d}A$. This regime represents a ‘front’ of sparse vortices, which are effectively point-like; in this range we predict $n(A)\sim t^{r_{3}-1}A^{-r_{3}}$. Allowing for time-varying $\overline{\unicode[STIX]{x1D714}_{v}^{2}}$ results in a small but significant correction to these temporal dependences. High-resolution numerical simulations verify the predicted vortex and spectral peak growth rates, as well as the theoretical picture of the three scaling ranges in the vortex population. Vortices steepen the energy spectrum $E(k)$ past the classical $k^{-5/3}$ scaling in the range $k\in [k_{f},k_{v}]$, where $k_{v}$ is the wavenumber associated with the largest vortex, while at larger scales the slope approaches $-5/3$. Though vortices disrupt the classical scaling, their number density distribution and evolution reveal deeper and more complex scale invariance, and suggest an effective theory of the inverse cascade in terms of vortex interactions.

Type
Papers
Copyright
© 2016 Cambridge University Press 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Batchelor, G. K. 1969 Computation of the energy spectrum in homogeneous two-dimensional turbulence. Phys. Fluids 12, 233239.Google Scholar
Benzi, R., Collela, M., Briscolini, M. & Santangelo, P. 1992 A simple point vortex model for two-dimensional decaying turbulence. Phys. Fluids. A 4, 10361039.Google Scholar
Benzi, R., Patarnello, S. & Santangelo, P. 1988 Self-similar coherent structures in two-dimensional decaying turbulence. J. Phys. A 5, 12211237.Google Scholar
Boffetta, G. 2007 Energy and enstrophy fluxes in the double cascade of two-dimensional turbulence. J. Fluid Mech. 589, 253260.Google Scholar
Boffetta, G., Celani, A. & Vergassola, M. 2000 Inverse energy cascade in two-dimensional turbulence: Deviations from Gaussian behavior. Phys. Rev. E 61, R29.Google Scholar
Boffetta, G. & Ecke, R. E. 2012 Two-dimensional turbulence. Annu. Rev. Fluid Mech. 44, 427451.Google Scholar
Boffetta, G. & Musacchio, S. 2010 Evidence for the double cascade scenario in two-dimensional turbulence. Phys. Rev. E 82, 016307.Google Scholar
Borue, V. 1994 Inverse energy cascade in stationary two-dimensional homogeneous turbulence. Phys. Rev. Lett. 72, 14751478.Google Scholar
Bruneau, C. H. & Kellay, H. 2005 Experiments and direct numerical simulations of two-dimensional turbulence. Phys. Rev. E 71, 046305.Google Scholar
Burgess, B. H., Scott, R. K. & Shepherd, T. G. 2015 Kraichnan–Leith–Batchelor similarity theory and two-dimensional inverse cascades. J. Fluid Mech. 767, 467496.Google Scholar
Carnevale, G. F., McWilliams, J. C., Pomeau, Y., Weiss, J. B. & Young, W. R. 1991 Evolution of vortex statistics in two-dimensional turbulence. Phys. Rev. Lett. 66, 27352738.Google Scholar
Dritschel, D. G., Scott, R. K., Macaskill, C., Gottwald, G. A. & Tran, C. V. 2008 Unifying scaling theory for vortex dynamics in two-dimensional turbulence. Phys. Rev. Lett. 101, 094501.Google Scholar
Dubos, T., Babiano, A., Paret, J. & Tabeling, P. 2001 Intermittency and coherent structures in the two-dimensional inverse energy cascade: comparing numerical and laboratory experiments. Phys. Rev. E 64, 036302.Google Scholar
Fontane, J., Dritschel, D. G. & Scott, R. K. 2013 Vortical control of forced two-dimensional turbulence. Phys. Fluids 25, 015101.Google Scholar
Frisch, U. & Sulem, P.-L. 1984 Numerical simulation of the inverse cascade in two-dimensional turbulence. Phys. Fluids 27, 19211923.Google Scholar
Kraichnan, R. H. 1967 Inertial ranges in two-dimensional turbulence. Phys. Fluids 10, 14171423.Google Scholar
Leith, C. E. 1968 Diffusion approximation for two-dimensional turbulence. Phys. Fluids 11, 671673.Google Scholar
Lowe, A. J. & Davidson, P. A. 2005 The evolution of freely decaying, isotropic, two-dimensional turbulence. Eur. J. Mech. (B/Fluids) 24, 314327.Google Scholar
Maltrud, M. E. & Vallis, G. K. 1991 Energy spectra and coherent structures in forced two-dimensional and beta-plane turbulence. J. Fluid Mech. 228, 321342.Google Scholar
Paret, J. & Tabeling, P. 1998 Intermittency in the inverse cascade. Phys. Fluids 10, 3126.Google Scholar
Rutgers, M. 1998 Forced 2D turbulence: experimental evidence of simultaneous inverse energy and forward enstrophy cascades. Phys. Rev. Lett. 11, 22442247.Google Scholar
Scott, R. K. 2007 Nonrobustness of the two-dimensional turbulent inverse cascade. Phys. Rev. E 75, 046301.Google Scholar
Smith, L. M. & Yakhot, V. 1993 Bose condensation and small-scale structure generation in a random force driven 2D turbulence. Phys. Rev. Lett. 71, 352355.Google Scholar
Sommeria, J. 1986 Experimental study of the two-dimensional inverse energy cascade in a square box. J. Fluid Mech. 170, 139168.Google Scholar
Tran, C. V. & Dritschel, D. G. 2006 Large-scale dynamics in two-dimensional Euler and surface quasigeostrophic flows. Phys. Fluids 18, 121703.Google Scholar
Vallgren, A. 2011 Infrared Reynolds number dependency of the two-dimensional inverse energy cascade. J. Fluid Mech. 667, 463473.Google Scholar
Weiss, J. B. & McWilliams, J. C. 1993 Temporal scaling behavior of decaying two-dimensional turbulence. Phys. Fluids 5, 608621.Google Scholar