Compression of magnetohydrodynamic simulation data using singular value decomposition
Compression of magnetohydrodynamic simulation data using singular value decomposition
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DOI：
10.1016/j.jcp.2006.07.022
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发表时间：
2007-03-01
影响因子：
4.1
通讯作者：
D'Azevedo, E. F.
中科院分区：
文献类型：
--
作者：
del-Castillo-Negrete, D.;Hirshman, S. P.;D'Azevedo, E. F.
在磁流体动力学（MHD）模拟中，磁场和流场的数值计算会产生大量的数据。在这些MHD领域中，需要提供MHD方程的闭合关系的基于粒子的计算，将需要将这些数据传送到多个处理器，并在许多粒子轨道位置处进行快速插值。为了便于这种分析，使用奇异值分解（SVD，或主正交分解，POD）方法压缩数据是有利的。作为压缩技术的一个例子，奇异值分解被应用于磁场数据所产生的动态非线性MHD代码。通过计算三维磁场中电子轨道的庞加莱图并与未压缩数据进行比较，分析了SVD压缩算法的性能。(c)2006年爱思唯尔公司All rights reserved.
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 Poincare plots for electron orbits in a three-dimensional magnetic field and comparing the results with uncompressed data. (c) 2006 Elsevier Inc. All rights reserved.