Stability and error analysis of IMEX-BDFk finite element schemes for the incompressible Navier-Stokes system
Qianqian Ding, Yifan Luo, Shipeng Mao
Abstract
In this paper, we propose and analyze a class of high-order numerical schemes within a fully discrete finite element framework for the incompressible Navier-Stokes equations with no-slip boundary conditions. The temporal discretization employs a kth-order (k=1,...,6) implicit-explicit backward difference formula (IMEX-BDFk), in which the nonlinear convection term is treated explicitly and the linear Stokes part implicitly, whereas the spatial discretization utilizes Taylor-Hood finite elements. We establish the stability and uniform boundedness of the numerical solution. We further establish optimal order error estimates in both space and time without any CFL-type condition, in the sense that the time step is independent of the spatial mesh size. In three dimensions, these include L2- and H1-norm error estimates for the velocity and L2-norm error estimates for the pressure, with temporal convergence rates up to sixth order for all variables. Numerical experiments are presented to demonstrate the effectiveness of the scheme and to confirm the theoretical convergence rates.
Create a lesson
Related papers
Graph lattice sums and graph zeta functions for long-range interacting quantum lattice models
Andreas Alexander Buchheit, Andreas Rupp
A numerical benchmark for fluid--structure--contact interaction
Daniele Corti, Jakub Fara, Miguel Angel Fernández et al.
Largest-dihedral-angle bisection algorithm does not preserve mesh regularity for tetrahedral partitions
Sergey Korotov, Jérôme Michaud
A Highly Scalable Quantized Tensor-Train FDTD Framework for the Simulation of Three-Dimensional Electromagnetic Scattering Problems
Daan Vanhaecke, Emile Vanderstraeten, Dries Vande Ginste
Pressure-robustness by commuting interpolation operators for Stokes discretizations with continuous pressures
Philip L. Lederer, Theresa Vock
A Reynolds-Semi-Robust, Globally Divergence-Free HDG Method for the Smagorinsky Model
Shuaijun Liu, Xiaoping Xie