A moving collocation method for solving time dependent partial differential equations

被引:52
|
作者
Huang, WZ [1 ]
Russell, RD [1 ]
机构
[1] SIMON FRASER UNIV,DEPT MATH & STAT,BURNABY,BC V5A 1S6,CANADA
基金
加拿大自然科学与工程研究理事会; 美国国家科学基金会;
关键词
D O I
10.1016/0168-9274(95)00119-0
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
A new moving mesh method is introduced for solving time dependent partial differential equations (PDEs) in divergence form. The method uses a cell averaging cubic Hermite collocation discretization for the physical PDEs and a three point finite difference discretization for the PDE which determines the moving mesh. Numerical results are presented for a selection of difficult bench-mark problems, including Burgers' equation and Sod's shocktube problem. They indicate third order convergence for the method, slower than the traditional (fourth order) cubic Hermite collocation on a fixed mesh but much faster than the first order of the commonly used moving finite difference methods. Numerical experiments also show that, in comparison with finite differences and fixed mesh collocation, moving collocation produces more accurate results for small and moderate numbers of mesh points.
引用
收藏
页码:101 / 116
页数:16
相关论文
共 50 条
  • [1] A robust moving mesh method for spectral collocation solutions of time-dependent partial differential equations
    Subich, Christopher J.
    JOURNAL OF COMPUTATIONAL PHYSICS, 2015, 294 : 297 - 311
  • [2] ADAPTIVE FUP COLLOCATION METHOD FOR TIME DEPENDENT PARTIAL DIFFERENTIAL EQUATIONS
    Gotovac, Hrvoje
    Kozulic, Vedrana
    Gotovac, Blaz
    Sesartic, Renata
    Brajcic, Nives
    Colak, Ivo
    ANNALS OF DAAAM FOR 2009 & PROCEEDINGS OF THE 20TH INTERNATIONAL DAAAM SYMPOSIUM, 2009, 20 : 1889 - 1890
  • [3] The neural network collocation method for solving partial differential equations
    Adam R. Brink
    David A. Najera-Flores
    Cari Martinez
    Neural Computing and Applications, 2021, 33 : 5591 - 5608
  • [4] The neural network collocation method for solving partial differential equations
    Brink, Adam R.
    Najera-Flores, David A.
    Martinez, Cari
    Neural Computing and Applications, 2021, 33 (11): : 5591 - 5608
  • [5] The neural network collocation method for solving partial differential equations
    Brink, Adam R.
    Najera-Flores, David A.
    Martinez, Cari
    NEURAL COMPUTING & APPLICATIONS, 2021, 33 (11): : 5591 - 5608
  • [6] Reduced Collocation Method for Time-Dependent Parametrized Partial Differential Equations
    Rezvan Ghaffari
    Farideh Ghoreishi
    Bulletin of the Iranian Mathematical Society, 2019, 45 : 1487 - 1504
  • [7] Reduced Collocation Method for Time-Dependent Parametrized Partial Differential Equations
    Ghaffari, Rezvan
    Ghoreishi, Farideh
    BULLETIN OF THE IRANIAN MATHEMATICAL SOCIETY, 2019, 45 (05) : 1487 - 1504
  • [8] On a new time integration method for solving time dependent partial differential equations
    Zhong, WX
    Zhu, JN
    Zhong, XX
    COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 1996, 130 (1-2) : 163 - 178
  • [9] A Collocation Method Using Radial Polynomials for Solving Partial Differential Equations
    Ku, Cheng-Yu
    Xiao, Jing-En
    SYMMETRY-BASEL, 2020, 12 (09):
  • [10] A Wavelet Method for Solving Nonlinear Time-Dependent Partial Differential Equations
    Liu, Xiaojing
    Wang, Jizeng
    Zhou, Youhe
    CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES, 2013, 94 (03): : 225 - 238