Some errors estimates for the box method
SIAM Journal on Numerical Analysis
Computer Methods in Applied Mechanics and Engineering - Special edition on the 20th Anniversary
A novel two-grid method for semilinear elliptic equations
SIAM Journal on Scientific Computing
Two-level Picard and modified Picard methods for the Navier-Stokes equations
Applied Mathematics and Computation
Two-grid Discretization Techniques for Linear and Nonlinear PDEs
SIAM Journal on Numerical Analysis
A Two-Grid Finite Difference Scheme for Nonlinear Parabolic Equations
SIAM Journal on Numerical Analysis
On the Accuracy of the Finite Volume Element Method Based on Piecewise Linear Polynomials
SIAM Journal on Numerical Analysis
Stabilization of Low-order Mixed Finite Elements for the Stokes Equations
SIAM Journal on Numerical Analysis
Two-grid finite volume element method for linear and nonlinear elliptic problems
Numerische Mathematik
A full discrete two-grid finite-volume method for a nonlinear parabolic problem
International Journal of Computer Mathematics
Mathematics and Computers in Simulation
On the semi-discrete stabilized finite volume method for the transient Navier---Stokes equations
Advances in Computational Mathematics
Hi-index | 7.29 |
In this paper we consider a two-level finite volume method for the two-dimensional unsteady Navier-Stokes equations by using two local Gauss integrations. This new stabilized finite volume method is based on the linear mixed finite element spaces. Some new a priori bounds for the approximate solution are derived. Moreover, a two-level stabilized finite volume method involves solving one small Navier-Stokes problem on a coarse mesh with mesh size H, a large general Stokes problem on the fine mesh with mesh size h@?H. The optimal error estimates of the H^1-norm for velocity approximation and the L^2-norm for pressure approximation are established. If we choose h=O(H^2), the two-level method gives the same order of approximation as the one-level stabilized finite volume method. However, our method can save a large amount of computational time.