[139.14] Supersonic Turbulence: Intermittency, and Simulations with Adaptive Meshes

AAS 205th Meeting, 9-13 January 2005
Session 139 The Ionized ISM: Observations and Theory
Poster, Thursday, January 13, 2005, 9:20am-4:00pm, Exhibit Hall

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[139.14] Supersonic Turbulence: Intermittency, and Simulations with Adaptive Meshes

A. G. Kritsuk, M. L. Norman, P. Padoan (UCSD)

We report first results from 3D numerical simulations of homogeneous supersonic hydrodynamic turbulence with the massively parallel adaptive mesh refinement (AMR) code Enzo. The code uses the Piecewise Parabolic Method (PPM, Woodward & Colella 1984) to solve the Euler equations in a box with periodic boundary conditions and effective grid resolution up to 1024³. Our model describes driven Mach 6 turbulence and assumes an isothermal equation of state, thus roughly approximating the conditions in molecular clouds. We use one level of mesh refinement by a factor of four on shocks and shear to follow the most important dissipative structures of turbulence with the highest resolution.

Exactly as predicted by a phenomenological intermittency model, as soon as the resolution is high enough to provide integral/dissipative scale separation (this happens at ~ 512³ zones for Euler turbulence simulations with PPM), the volume filling factor for subgrids scales as Re^3D/(1+D)/Re^9/4, where D=2 is the dimension of the dissipative structures and Re is the Reynolds number. This scaling makes AMR simulations of high-Re flows more efficient than equivalent simulations on uniform grids.

We compared PDFs and 3D power spectra of gas density as well as 3D velocity power spectra from our AMR and unigrid runs and found them in excellent agreement with each other. Based on these results, we discuss the signature of dissipative structures in the statistical properties of supersonic turbulence and their role in overall flow dynamics.

This work was partially supported by NRAC allocation MCA098020S and utilized computing resources provided by the San Diego Supercomputer Center. We would also like to thank Sun Microsystems' RASCAL Lab for the opportunity to evaluate their new high-availability framework for scientific computing.

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