Astronomy and Astrophysics – Astrophysics
Scientific paper
Feb 2004
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004a%26a...414..807w&link_type=abstract
Astronomy and Astrophysics, v.414, p.807-824 (2004)
Astronomy and Astrophysics
Astrophysics
2
Galaxies: Magnetic Fields, Ism: Magnetic Fields, Magnetic Fields, Methods: Numerical, Mhd, Turbulence
Scientific paper
We present the results of a numerical investigation of the turbulent kinematic dynamo problem in a high Prandtl number regime. The scales of the magnetic turbulence we consider are far smaller than the Kolmogorov dissipative scale, so that the magnetic wavepackets evolve in a nearly smooth velocity field. Firstly, we use a stochastic Euler-Maruyama method to simulate the Kraichnan-Kazantsev model (KKM) in which the strain matrix is taken to be independent of coordinate and Gaussian white in time. We test the theoretical predictions for the growth of rates of the magnetic energy and higher order moments \citep{Kazantsev,Chertkov,Kulsrud}, the shape of the energy spectrum \citep{Schekochihin,Kazantsev,NazWesZab,Kulsrud} and the behavior of the polarization and spectral flatness, new measures introduced in \cite{NazWesZab}. In general, the results appear to be in good agreement with the theory, with the exception that the predicted decay of the polarization in time is not reproduced well in the stochastic numerics. Secondly, in order to study the sensitivity of the KKM predictions to the choice of strain statistics, we perform additional simulations for the case of a Gaussian strain with a finite correlation time and also for a strain taken from a DNS data set. These experiments are based on non-stochastic schemes, using a timestep that is much smaller than the correlation time of the strain. We find that the KKM is generally insensitive to the choice of strain statistics and most KKM results, including the decay of the polarization, are reproduced well. The only exception appears to be the flatness whose spectrum is not reproduced in accordance with the KKM predictions in these simulations.
Galtier Sebastien
Laval Jean-Philippe
Nazarenko Sergey
West Jacob R.
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