Compression of magnetohydrodynamic simulation data using singular value decomposition
Abstract
Numerical calculations of magnetic and flow fields in magnetohydrodynamic (MHD) simulations can result in extensive data sets. Particlebased 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 Poincare plots for electron orbits in a threedimensional magnetic field and comparing the results with uncompressed data.
 Authors:
 Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)
 Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States). Email: hirshmansp@ornl.gov
 Publication Date:
 OSTI Identifier:
 20991563
 Resource Type:
 Journal Article
 Resource Relation:
 Journal Name: Journal of Computational Physics; Journal Volume: 222; Journal Issue: 1; Other Information: DOI: 10.1016/j.jcp.2006.07.022; PII: S00219991(06)00355X; Copyright (c) 2006 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved; Country of input: International Atomic Energy Agency (IAEA)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ALGORITHMS; APPROXIMATIONS; COMPUTERIZED SIMULATION; DATA TRANSMISSION; EQUATIONS; INTERPOLATION; MAGNETIC FIELDS; MAGNETOHYDRODYNAMICS; NONLINEAR PROBLEMS; ORBITS; PERFORMANCE; THREEDIMENSIONAL CALCULATIONS
Citation Formats
Castillo Negrete, D. del, Hirshman, S.P., Spong, D.A., and D'Azevedo, E.F. Compression of magnetohydrodynamic simulation data using singular value decomposition. United States: N. p., 2007.
Web. doi:10.1016/j.jcp.2006.07.022.
Castillo Negrete, D. del, Hirshman, S.P., Spong, D.A., & D'Azevedo, E.F. Compression of magnetohydrodynamic simulation data using singular value decomposition. United States. doi:10.1016/j.jcp.2006.07.022.
Castillo Negrete, D. del, Hirshman, S.P., Spong, D.A., and D'Azevedo, E.F. Thu .
"Compression of magnetohydrodynamic simulation data using singular value decomposition". United States.
doi:10.1016/j.jcp.2006.07.022.
@article{osti_20991563,
title = {Compression of magnetohydrodynamic simulation data using singular value decomposition},
author = {Castillo Negrete, D. del and Hirshman, S.P. and Spong, D.A. and D'Azevedo, E.F.},
abstractNote = {Numerical calculations of magnetic and flow fields in magnetohydrodynamic (MHD) simulations can result in extensive data sets. Particlebased 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 Poincare plots for electron orbits in a threedimensional magnetic field and comparing the results with uncompressed data.},
doi = {10.1016/j.jcp.2006.07.022},
journal = {Journal of Computational Physics},
number = 1,
volume = 222,
place = {United States},
year = {Thu Mar 01 00:00:00 EST 2007},
month = {Thu Mar 01 00:00:00 EST 2007}
}

Numerical calculations of magnetic and flow fields in magnetohydrodynamic (MHD) simulations can result in extensive data sets. Particlebased 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 ismore »

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