期刊
JOURNAL OF COMPUTATIONAL PHYSICS
卷 230, 期 23, 页码 8508-8526出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcp.2011.08.002
关键词
Massively parallel computation; Electromagnetic particle-in-cell model; Adaptive mesh refinement; Multi-scale phenomenon; Magnetic reconnection
资金
- STE Laboratory, Nagoya University
- RISH, Kyoto University
- JHPCN (Japan High Performance Computing and Networking)
- JSPS [21840061]
- Grants-in-Aid for Scientific Research [23740373, 21840061] Funding Source: KAKEN
A new electromagnetic particle-in-cell (EMPIC) model with adaptive mesh refinement (AMR) has been developed to achieve high-performance parallel computation in distributed memory system. For minimizing the amount and frequency of inter-processor communications, the present study uses the staggering grid scheme with the charge conservation method, which consists only of the local operations. However, the scheme provides no numerical damping for electromagnetic waves regardless of the wavenumber, which results in significant noise in the refinement region that eventually covers over physical signals. In order to suppress the electromagnetic noise, the present study introduces a smoothing method which gives numerical damping preferentially for short wavelength modes. The test simulations show that only a weak smoothing results in drastic reduction in the noise, so that the implementation of the AMR is possible in the staggering grid scheme. The computational load balance among the processors is maintained by a new method termed the adaptive block technique for the domain decomposition parallelization. The adaptive block technique controls the subdomain (block) structure dynamically associated with the system evolution, such that all the blocks have almost the same number of particles. The performance of the present code is evaluated for the simulations of the current sheet evolution. The test simulations demonstrate that the usage of the adaptive block technique as well as the staggering grid scheme enhances significantly the parallel efficiency of the AMR-EMPIC model. (C) 2011 Elsevier Inc. All rights reserved.
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