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二维各向异性磁等离子体的无条件稳定ADE-CNAD-FDTD算法

李建雄 庄永佳 李现国

李建雄, 庄永佳, 李现国. 二维各向异性磁等离子体的无条件稳定ADE-CNAD-FDTD算法[J]. 强激光与粒子束, 2018, 30: 012001. doi: 10.11884/HPLPB201830.170269
引用本文: 李建雄, 庄永佳, 李现国. 二维各向异性磁等离子体的无条件稳定ADE-CNAD-FDTD算法[J]. 强激光与粒子束, 2018, 30: 012001. doi: 10.11884/HPLPB201830.170269
Li Jianxiong, Zhuang Yongjia, Li Xianguo. Unconditionally stable auxiliary differential equation Crank-Nicolson-approximate-decoupling FDTD algorithm for 2-D anisotropic magnetized plasma[J]. High Power Laser and Particle Beams, 2018, 30: 012001. doi: 10.11884/HPLPB201830.170269
Citation: Li Jianxiong, Zhuang Yongjia, Li Xianguo. Unconditionally stable auxiliary differential equation Crank-Nicolson-approximate-decoupling FDTD algorithm for 2-D anisotropic magnetized plasma[J]. High Power Laser and Particle Beams, 2018, 30: 012001. doi: 10.11884/HPLPB201830.170269

二维各向异性磁等离子体的无条件稳定ADE-CNAD-FDTD算法

doi: 10.11884/HPLPB201830.170269
基金项目: 

国家自然科学基金项目 61372011

详细信息
    作者简介:

    李建雄(1969—),男,博士,教授,主要从事计算电磁学的研究;lijianxiong@tjpu.edu.cn

    通讯作者:

    李现国(1981—),男,博士,副教授,主要从事算法研究;lixianguo@tjpu.edu.cn

  • 中图分类号: O.441.4

Unconditionally stable auxiliary differential equation Crank-Nicolson-approximate-decoupling FDTD algorithm for 2-D anisotropic magnetized plasma

  • 摘要: 针对二维各向异性磁等离子体提出一种有效的无条件稳定算法,新算法结合了辅助微分方程(ADE)方法与Crank-Nicolson approximate-decoupling(CNAD)时域有限差分算法仿真各向异性磁等离子体介质。传统的ADE-FDTD方法应用在一维各向异性色散介质具有较高的精度和效率,将提出的新算法ADE-CNAD-FDTD应用到二维各向异性磁等离子体介质中不仅解决了电磁波在具有各向异性和频率色散特性介质中传播的仿真难题,而且去除了CFL稳定性条件。该算法在保留了原有的精度情况下大幅度地提高了计算效率并成为无条件稳定的形式。给出一个算例证明该算法的有效性,通过模拟电磁波在磁等离子体中的传播,仿真结果与传统的ADE-FDTD算法对比,证实了该算法的高效率、无条件稳定性和高精度。
  • 图  1  比较ADE-CNAD-FDTD在不同CFLN值情况下与传统ADE-FDTD的电场强度Ex

    Figure  1.  Comparison of electric field intensity Ex between ADE-CNAD-FDTD with different CFLNs and conventional ADE-FDTD

    图  2  比较ADE-CNAD-FDTD在不同CFLN值情况下与传统ADE-FDTD的电场强度Ey

    Figure  2.  Comparison of electric field intensity Ey between ADE-CNAD-FDTD with different CFLNs and conventional ADE-FDTD

    表  1  传统ADE-FDTD和ADE-CNAD-FDTD两种算法占用的时间和内存

    Table  1.   Time and memory used by conventional ADE-FDTD method and ADE-CNAD-FDTD method

    ADE-FDTD ADE-CNAD-FDTD
    CFLN=1 CFLN=2 CFLN=4 CFLN=6
    time/s 78.880 167.650 80.870 40.850 27.340
    memory/MB 82.604 90.844 90.872 90.760 90.876
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-06-29
  • 修回日期:  2017-09-22
  • 刊出日期:  2018-01-15

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