An adaptive time-stepping Fourier pseudo-spectral method for the Zakharov-Rubenchik equation

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED
Bingquan Ji, Xuanxuan Zhou
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引用次数: 0

Abstract

An adaptive time-stepping scheme is developed for the Zakharov-Rubenchik system to resolve the multiple time scales accurately and to improve the computational efficiency during long-time simulations. The Crank-Nicolson formula and the Fourier pseudo-spectral method are respectively utilized for the temporal and spatial approximations. The proposed numerical method is proved to preserve the mass and energy conservative laws in the discrete levels exactly so that the magnetic field, the density of mass, and the fluid speed are stable on a general class of nonuniform time meshes. With the aid of the priori estimates derived from the discrete invariance and the newly proved discrete Gronwall inequality on variable time grids, sharp convergence analysis of the fully discrete scheme is established rigorously. Error estimate shows that the suggested adaptive time-stepping method can attain the second-order accuracy in time and the spectral accuracy in space. Extensive numerical experiments coupled with an adaptive time-stepping algorithm are presented to show the effectiveness of our numerical method in capturing the multiple time scale evolution for various velocity cases during the interactions of solitons.

扎哈罗夫-鲁本奇克方程的自适应时间步进傅立叶伪谱方法
针对 Zakharov-Rubenchik 系统开发了一种自适应时间步进方案,以精确解决多时间尺度问题,并提高长时间模拟的计算效率。时间和空间近似分别采用了 Crank-Nicolson 公式和傅立叶伪谱法。实验证明,所提出的数值方法能在离散层面上精确地保留质量和能量守恒定律,从而使磁场、质量密度和流体速度在一般的非均匀时间网格上保持稳定。借助离散不变性的先验估计和新近证明的可变时间网格上的离散格伦沃尔不等式,严格建立了完全离散方案的尖锐收敛分析。误差估计表明,建议的自适应时间步进方法可以达到时间上的二阶精度和空间上的谱精度。广泛的数值实验与自适应时间步进算法相结合,展示了我们的数值方法在捕捉孤子相互作用过程中各种速度情况下的多时间尺度演化方面的有效性。
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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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