A New Implementation of the Magnetohydrodynamics-Relaxation Method for Nonlinear Force-Free Field Extrapolation in the Solar Corona

Magnetic field in the solar corona is usually extrapolated from photospheric vector magnetogram using a nonlinear force-free field (NLFFF) model. NLFFF extrapolation needs a considerable effort to be devoted for its numerical realization. In this paper we present a new implementation of the magnetohydrodynamics (MHD)-relaxation method for NLFFF extrapolation. The magneto-frictional approach which is introduced for speeding the relaxation of the MHD system is novelly realized by the spacetime conservation-element and solution-element (CESE) scheme. A magnetic field splitting method is used to further improve the computational accuracy. The bottom boundary condition is prescribed by changing the transverse field incrementally to match the magnetogram, and all other artificial boundaries of the computational box are simply fixed. We examine the code by two types of NLFFF benchmark tests, the Low & Lou (1990) semi-analytic force-free solutions and a more realistic solar-like case constructed by van Ballegooijen et al. (2007). The results show that our implementation are successful and versatile for extrapolations of either the relatively simple cases or the rather complex cases which need significant rebuilding of the magnetic topology, e.g., a flux rope. We also compute a suite of metrics to quantitatively analyze the results and demonstrate that the performance of our code in extrapolation accuracy basically reaches the same level of the present best-performing code, e.g., that developed by Wiegelmann (2004).Comment: Accept by ApJ, 45 pages, 13 figure

Distributed Branching Bisimulation Minimization by Inductive Signatures

We present a new distributed algorithm for state space minimization modulo branching bisimulation. Like its predecessor it uses signatures for refinement, but the refinement process and the signatures have been optimized to exploit the fact that the input graph contains no tau-loops. The optimization in the refinement process is meant to reduce both the number of iterations needed and the memory requirements. In the former case we cannot prove that there is an improvement, but our experiments show that in many cases the number of iterations is smaller. In the latter case, we can prove that the worst case memory use of the new algorithm is linear in the size of the state space, whereas the old algorithm has a quadratic upper bound. The paper includes a proof of correctness of the new algorithm and the results of a number of experiments that compare the performance of the old and the new algorithms