The small-scale turbulent dynamo in smoothed particle magnetohydrodynamics

10th International Conference on Numerical Modeling of Space Plasma Flows: ASTRONUM-2015

Tricco, T.S.^1,2,3 ; Price, D.J.^3 ; Federrath, C.^3,4

Canadian Institute for Theoretical Astrophysics, University of Toronto, 60 St. George Street, Toronto

ON

M5S 3H8, Canada^1

School of Physics, University of Exeter, Stocker Road, Exeter

EX4 4QL, United Kingdom^2

Monash Centre for Astrophysics, Monash University, Clayton

VIC

3800, Australia^3

Research School of Astronomy and Astrophysics, Australian National University, Canberra

ACT

2611, Australia^4

关键词: Computational expense;    Divergence cleaning;    Dynamo calculations;    Grid based method;    Machine precision;    Magnetic energies;    Numerical dissipation;    Supersonic turbulence;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/719/1/012003/pdf
DOI  :  10.1088/1742-6596/719/1/012003

来源: IOP

【 摘 要 】

Supersonic turbulence is believed to be at the heart of star formation. We have performed smoothed particle magnetohydrodynamics (SPMHD) simulations of the small- scale dynamo amplification of magnetic fields in supersonic turbulence. The calculations use isothermal gas driven at rms velocity of Mach 10 so that conditions are representative of starforming molecular clouds in the Milky Way. The growth of magnetic energy is followed for 10 orders in magnitude until it reaches saturation, a few percent of the kinetic energy. The results of our dynamo calculations are compared with results from grid-based methods, finding excellent agreement on their statistics and their qualitative behaviour. The simulations utilise the latest algorithmic developments we have developed, in particular, a new divergence cleaning approach to maintain the solenoidal constraint on the magnetic field and a method to reduce the numerical dissipation of the magnetic shock capturing scheme. We demonstrate that our divergence cleaning method may be used to achieve ∇ • B = 0 to machine precision, albeit at significant computational expense.