XIAO Ying-xiong, LI Zhen-you. Preconditioned Conjugate Gradient Methods for the 3D Wilson Nonconforming FEM Discretizations[J]. Applied Mathematics and Mechanics, 2016, 37(8): 820-831. doi: 10.21656/1000-0887.370037
Citation: XIAO Ying-xiong, LI Zhen-you. Preconditioned Conjugate Gradient Methods for the 3D Wilson Nonconforming FEM Discretizations[J]. Applied Mathematics and Mechanics, 2016, 37(8): 820-831. doi: 10.21656/1000-0887.370037

Preconditioned Conjugate Gradient Methods for the 3D Wilson Nonconforming FEM Discretizations

doi: 10.21656/1000-0887.370037
Funds:  The National Natural Science Foundation of China(10972191)
  • Received Date: 2016-01-25
  • Rev Recd Date: 2016-03-16
  • Publish Date: 2016-08-15
  • The nonconforming finite element method (FEM) is an efficient method to overcome the volume locking trouble in 3D elasticity problems. This method has the advantages of a few degrees of freedom and high accuracy. In order to improve the overall efficiency of the FEM analysis, it is necessary to design some faster solvers for the corresponding system of discretization equations. The faster solvers for the Wilson nonconforming FEM discretizations were considered. When Poisson’s ratio ν was close to 0.5, the resulting system of equations was symmetric positive definite and highly illconditioned, and the preconditioned conjugate gradient (PCG) method was one of the most efficient methods for solving such FEM equations. Moreover, in practical applications, anisotropic meshes are often obtained due to the specificity of the structure considered, which will greatly decrease the convergence rate of the PCG method. A type of PCG method based on the DAMG was presented and then applied to the solution of the Wilson FEM discretizations. This DAMG was an algebraic multi grid (AMG) method based on the distance matrix and can be used to solve the system of equations discretized on anisotropic meshes. The numerical results show that, in combination with the effective smoothing operators, the proposed PCG method has high efficiency and robustness for nearly incompressible problems.
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