Relaxation equations for two-dimensional turbulent flows with a prior vorticity distribution

Physics – Fluid Dynamics

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

21 pages, 9 figures

Scientific paper

10.1140/epjb/e2010-00264-5

Using a Maximum Entropy Production Principle (MEPP), we derive a new type of relaxation equations for two-dimensional turbulent flows in the case where a prior vorticity distribution is prescribed instead of the Casimir constraints [Ellis, Haven, Turkington, Nonlin., 15, 239 (2002)]. The particular case of a Gaussian prior is specifically treated in connection to minimum enstrophy states and Fofonoff flows. These relaxation equations are compared with other relaxation equations proposed by Robert and Sommeria [Phys. Rev. Lett. 69, 2776 (1992)] and Chavanis [Physica D, 237, 1998 (2008)]. They can provide a small-scale parametrization of 2D turbulence or serve as numerical algorithms to compute maximum entropy states with appropriate constraints. We perform numerical simulations of these relaxation equations in order to illustrate geometry induced phase transitions in geophysical flows.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Relaxation equations for two-dimensional turbulent flows with a prior vorticity distribution does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Relaxation equations for two-dimensional turbulent flows with a prior vorticity distribution, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Relaxation equations for two-dimensional turbulent flows with a prior vorticity distribution will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-568670

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.