The idea of 'frozen-in' magnetic field lines for ideal plasmas is useful to explain diverse astrophysical phenomena, for example the shedding of excess angular momentum from protostars by twisting of field lines frozen into the interstellar medium. Frozen-in field lines, however, preclude the rapid changes in magnetic topology observed at high conductivities, as in solar flares. Microphysical plasma processes are a proposed explanation of the observed high rates, but it is an open question whether such processes can rapidly reconnect astrophysical flux structures much greater in extent than several thousand ion gyroradii. An alternative explanation is that turbulent Richardson advection brings field lines implosively together from distances far apart to separations of the order of gyroradii. Here we report an analysis of a simulation of magnetohydrodynamic turbulence at high conductivity that exhibits Richardson dispersion. This effect of advection in rough velocity fields, which appear non-differentiable in space, leads to line motions that are completely indeterministic or 'spontaneously stochastic', as predicted in analytical studies. The turbulent breakdown of standard flux freezing at scales greater than the ion gyroradius can explain fast reconnection of very large-scale flux structures, both observed (solar flares and coronal mass ejections) and predicted (the inner heliosheath, accretion disks, γ-ray bursts and so on). For laminar plasma flows with smooth velocity fields or for low turbulence intensity, stochastic flux freezing reduces to the usual frozen-in condition.

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A recently developed public turbulence database system (http://turbulence.pha.jhu.edu) provides new ways to access large datasets generated from high-performance computer simulations of turbulent flows to perform numerical experiments. The database archives 1024 4 (spatial and time) data points obtained from a pseudo-spectral direct numerical simulation (DNS) of forced isotropic turbulence. The flow's Taylor-scale Reynolds number is Re λ = 443, and the simulation output spans about one large-scale eddy turnover time. Besides the stored velocity and pressure fields, built-in first-and second-order space differentiation, as well as spatial and temporal interpolations are implemented on the database. The resulting 27 terabytes of data are public and can be accessed remotely through an interface based on a modern Web-services model. Users may write and execute analysis programs on their host computers, while the programs make subroutine-like calls (getFunctions) requesting desired variables (velocity and pressure, and their gradients) over the network. The architecture of the database and the initial built-in functionalities are described in a previous paper of Journal of Turbulence (A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence, J. Turbul. 9 (2008), p. N31). In the present paper, further developments of the database system are described; mainly the newly developed getPosition function. Given an initial position, integration time-step, as well as an initial and end time, the getPosition function tracks arrays of fluid particles and returns particle locations at the end of the trajectory integration time. The getPosition function is tested by comparing with trajectories computed outside of the database. It is then applied to study the Lagrangian velocity structure functions as well as the tensor-based Lagrangian time correlation functions. The roles of pressure Hessian and viscous terms in the evolution of the symmetric and antisymmetric parts of the velocity gradient tensor are explored by comparing the time correlations with and without these terms. Besides the getPosition function, several other updates to the database are described such as a function to access the forcing term in the DNS, a new more efficient interpolation algorithm based on partial sums, and a new Matlab interface.

The Johns Hopkins Turbulence Databases are an open simulation laboratory for the study of turbulence. They provide an immersive environment in which world-class numerical simulation datasets are available "at your fingertips." Such an environment has the potential to transform our understanding of turbulent flows. M ost, if not all, science disciplines have computational branches-computational physics, computational biology, computational chemistry, and computational fluid dynamics, to name a few. In some cases, experimentation simply isn't possible, and in others, it might be too expensive or too dangerous. In the case of fluid dynamics in particular, direct numerical simulations (DNSs) are widely adopted tools for developing and refining the turbulence models that improve our understanding of how the underlying physical processes work. The Navier-Stokes equations that govern turbulent fluid flow are discretized and integrated forward in time, solving for physical field variables (velocity and pressure) as functions of space and time in the simulation's domain.The memory and computational requirements of turbulence DNSs scale as the 9/4th and third power of the nondimensional Reynolds number, Re. 1 Turbulent flows usually have high Re and must therefore be solved with supercomputers. Preparing and running such a simulation requires substantial expertise in parallel computing and turbulence research and, moreover, access to a supercomputing facility. Traditionally, such undertakings have been team efforts, planned and prepared for ahead of time, with the particular science questions to be answered thought out in advance and most of the analysis done during the

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