Interaction of Large and Smallscale Dynamos in Isotropic Turbulent Flows from GPUaccelerated Simulations
Abstract
Magnetohydrodynamical (MHD) dynamos emerge in many different astrophysical situations where turbulence is present, but the interaction between largescale dynamos (LSDs) and smallscale dynamos (SSDs) is not fully understood. We performed a systematic study of turbulent dynamos driven by isotropic forcing in isothermal MHD with magnetic Prandtl number of unity, focusing on the exponential growth stage. Both helical and nonhelical forcing was employed to separate the effects of LSD and SSD in a periodic domain. Reynolds numbers ( ${\mathrm{Re}}_{{\rm{M}}}$ ) up to ≈250 were examined and multiple resolutions used for convergence checks. We ran our simulations with the Astaroth code, designed to accelerate 3D stencil computations on graphics processing units (GPUs) and to employ multiple GPUs with peertopeer communication. We observed a speedup of ≈35 in singlenode performance compared to the widely used multiCPU MHD solver Pencil Code. We estimated the growth rates from both the averaged magnetic fields and their power spectra. At low ${\mathrm{Re}}_{{\rm{M}}}$ LSD growth dominates, but at high ${\mathrm{Re}}_{{\rm{M}}}$ SSD appears to dominate in both helically and nonhelically forced cases. Pure SSD growth rates follow a logarithmic scaling as a function of ${\mathrm{Re}}_{{\rm{M}}}$ . Probability density functions of the magnetic field from the growth stage exhibit SSD behavior in helically forced cases even at intermediate ${\mathrm{Re}}_{{\rm{M}}}$ . We estimated mean field turbulence transport coefficients using closures like the secondorder correlation approximation (SOCA). They yield growth rates similar to the directly measured ones and provide evidence of α quenching. Our results are consistent with the SSD inhibiting the growth of the LSD at moderate ${\mathrm{Re}}_{{\rm{M}}}$ , while the dynamo growth is enhanced at higher ${\mathrm{Re}}_{{\rm{M}}}$ .
 Publication:

The Astrophysical Journal
 Pub Date:
 February 2021
 DOI:
 10.3847/15384357/abceca
 arXiv:
 arXiv:2012.08758
 Bibcode:
 2021ApJ...907...83V
 Keywords:

 Magnetic fields;
 Magnetohydrodynamics;
 Astrophysical fluid dynamics;
 Computational methods;
 GPU computing;
 994;
 1964;
 101;
 1965;
 1969;
 Physics  Fluid Dynamics;
 Astrophysics  Solar and Stellar Astrophysics;
 Physics  Computational Physics
 EPrint:
 22 pages, 23 figures, 2 tables, Accepted for publication in the Astrophysical Journal