A scalable, fully implicit algorithm for the reduced two-field low-β extended MHD model [electronic resource].
- Washington, D.C. : United States. Dept. of Energy. Office of Advanced Scientific Computing Research, 2016. and Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- 763-772 : digital, PDF file
- Additional Creators:
- Los Alamos National Laboratory, United States. Department of Energy. Office of Advanced Scientific Computing Research, and United States. Department of Energy. Office of Scientific and Technical Information
- Restrictions on Access:
- Free-to-read Unrestricted online access
- Here, we demonstrate a scalable fully implicit algorithm for the two-field low-β extended MHD model. This reduced model describes plasma behavior in the presence of strong guide fields, and is of significant practical impact both in nature and in laboratory plasmas. The model displays strong hyperbolic behavior, as manifested by the presence of fast dispersive waves, which make a fully implicit treatment very challenging. In this study, we employ a Jacobian-free Newton–Krylov nonlinear solver, for which we propose a physics-based preconditioner that renders the linearized set of equations suitable for inversion with multigrid methods. As a result, the algorithm is shown to scale both algorithmically (i.e., the iteration count is insensitive to grid refinement and timestep size) and in parallel in a weak-scaling sense, with the wall-clock time scaling weakly with the number of cores for up to 4096 cores. For a 4096 × 4096 mesh, we demonstrate a wall-clock-time speedup of ~6700 with respect to explicit algorithms. The model is validated linearly (against linear theory predictions) and nonlinearly (against fully kinetic simulations), demonstrating excellent agreement.
- Published through SciTech Connect., 12/01/2016., "la-ur-16-23169", Journal of Computational Physics 326 C ISSN 0021-9991 AM, and Luis Chacon; Adam John Stanier.
- Funding Information:
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