Published January 2012 | Version v1
Journal article

Improving parallel scalability for edge plasma transport simulations with neutral gas species

  • 1. Center for Applied Mathematics, 657 Rhodes Hall, Cornell University, Ithaca, NY 14853 (United States)
  • 2. Fusion Energy Sciences Program, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550 (United States)
  • 3. Argonne National Laboratory, Mathematics and Computer Science Division, 9700 South Cass Avenue, Argonne, IL 60439 (United States)

Description

Simulating the transport of multi-species plasma and neutral species in the edge region of a tokamak magnetic fusion energy device is computationally intensive and difficult due to coupling among various components, strong nonlinearities and a broad range of temporal scales. In addition to providing boundary conditions for the core plasma, such models aid in the understanding and control of the associated plasma/material-wall interactions, a topic that is essential for the development of a viable fusion power plant. The governing partial differential equations are discretized to form a large nonlinear system that typically must be evolved in time to obtain steady-state solutions. Fully implicit techniques using preconditioned Jacobian-free Newton-Krylov methods with parallel domain-based preconditioners are shown to be robust and efficient for the plasma components. Inclusion of neutral gas components, however, increases the condition number of the system to the point where improved parallel preconditioning is needed. Standard algebraic preconditioners that provide sufficient coupling throughout the global domain to handle the neutrals are not generally scalable. We present a new preconditioner, termed FieldSplit, which exploits the character of the neutral equations to improve the scalability of the combined plasma/neutral system. (paper)

Availability note (English)

Available from http://dx.doi.org/10.1088/1749-4699/5/1/014012

Additional details

Identifiers

Publishing Information

Journal Title
Computational Science and Discovery
Journal Volume
5
Journal Issue
1
Journal Page Range
[22 p.]
ISSN
1749-4699