Unconditional convergence and optimal L2 error estimates of the Crank-Nicolson extrapolation FEM for the nonstationary Navier-Stokes equations

作者：Guo, Yingwen; He, Yinnian^*

来源：Computers & Mathematics with Applications, 2018, 75(1): 134-152.

DOI：10.1016/j.camwa.2017.08.034

• Unconditional convergence
• Crank-Nicolson extrapolation
• Fully discrete mixed finite element
• Navier-Stokes equations

In this paper, we study stability and convergence of fully discrete finite element method on large timestep which used Crank-Nicolson extrapolation scheme for the nonstationary Navier-Stokes equations. This approach bases on a finite element approximation for the space discretization and the Crank-Nicolson extrapolation scheme for the time discretization. It reduces nonlinear equations to linear equations, thus can greatly increase the computational efficiency. We prove that this method is unconditionally stable and unconditionally convergent. Moreover, taking the negative norm technique, we derive the L-2, H-1-unconditionally optimal error estimates for the velocity, and the L-2-unconditionally optimal error estimate for the pressure. Also, numerical simulations on unconditional L-2-stability and convergent rates of this method are shown.

• 出版日期2018-1-1
• 单位西安交通大学