Compression of magnetohydrodynamic simulation data using singular value decomposition

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 an...

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Bibliographic Details
Published in:Journal of computational physics 2007-03, Vol.222 (1), p.265-286
Main Authors: del-Castillo-Negrete, D., Hirshman, S.P., Spong, D.A., D’Azevedo, E.F.
Format: Article
Language:English
Subjects:
ALGORITHMS
APPROXIMATIONS
CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
Computational techniques
COMPUTERIZED SIMULATION
Data compression
DATA TRANSMISSION
EQUATIONS
Exact sciences and technology
Generalized low rank approximation
INTERPOLATION
MAGNETIC FIELDS
MAGNETOHYDRODYNAMICS
Magnetohydronamics
Mathematical methods in physics
NONLINEAR PROBLEMS
Numerical methods
ORBITS
Physics
Singular value decomposition
THREE-DIMENSIONAL CALCULATIONS
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Summary: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.
ISSN:0021-9991
1090-2716
DOI:10.1016/j.jcp.2006.07.022