期刊
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
卷 304, 期 -, 页码 1-25出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2016.01.019
关键词
Stabilized FE; Variational multiscale methods; Resistive magnetohydrodynamics; Implicit methods; Newton-Krylov; Algebraic multigrid methods
资金
- DOE NNSA ASC
- DOE Office of Science AMR program at Sandia National Laboratory [DE-AC04-94AL85000]
- Los Alamos National Laboratory [DE-AC52-06NA25396]
The computational solution of the governing balance equations for mass, momentum, heat transfer and magnetic induction for resistive magnetohydrodynamics (MHD) systems can be extremely challenging. These difficulties arise from both the strong nonlinear, nonsymmetric coupling of fluid and electromagnetic phenomena, as well as the significant range of time-and length-scales that the interactions of these physical mechanisms produce. This paper explores the development of a scalable, fully-implicit stabilized unstructured finite element (FE) capability for 3D incompressible resistive MHD. The discussion considers the development of a stabilized FE formulation in context of the variational multiscale (VMS) method, and describes the scalable implicit time integration and direct-to-steady-state solution capability. The nonlinear solver strategy employs Newton-Krylov methods, which are preconditioned using fully-coupled algebraic multilevel preconditioners. These preconditioners are shown to enable a robust, scalable and efficient solution approach for the large-scale sparse linear systems generated by the Newton linearization. Verification results demonstrate the expected order-of-accuracy for the stabilized FE discretization. The approach is tested on a variety of prototype problems, that include MHD duct flows, an unstable hydromagnetic Kelvin-Helmholtz shear layer, and a 3D island coalescence problem used to model magnetic reconnection. Initial results that explore the scaling of the solution methods are also presented on up to 128K processors for problems with up to 1.8B unknowns on a CrayXK7. Published by Elsevier B.V.
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