Abstract
Numerical calculations of magnetic and flow fields in magnetohydrodynamic (MHD) simulations can result in extensive data sets. Particle-based calculations in these MHD fields, needed to provide closure relations for the MHD equations, will require communication of this data to multiple processors and rapid interpolation at numerous particle orbit positions. To facilitate this analysis it is advantageous to compress the data using singular value decomposition (SVD, or principal orthogonal decomposition, POD) methods. As an example of the compression technique, SVD is applied to magnetic field data arising from a dynamic nonlinear MHD code. The performance of the SVD compression algorithm is analyzed by calculating Poincaré plots for electron orbits in a three-dimensional magnetic field and comparing the results with uncompressed data.
| Original language | English |
|---|---|
| Pages (from-to) | 265-286 |
| Number of pages | 22 |
| Journal | Journal of Computational Physics |
| Volume | 222 |
| Issue number | 1 |
| DOIs | |
| State | Published - Mar 1 2007 |
Funding
Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory (ORNL), managed by UT-Battelle, LLC for the US Department of Energy under Contract No. DE-AC05-00OR22725. This research used resources of the National Center for Computational Sciences at Oak Ridge National Laboratory, which is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-00OR22725.
| Funders | Funder number |
|---|---|
| U.S. Department of Energy | DE-AC05-00OR22725 |
| Office of Science | |
| Oak Ridge National Laboratory | |
| UT-Battelle |
Keywords
- Data compression
- Generalized low rank approximation
- Magnetohydronamics
- Numerical methods
- Singular value decomposition