Preconditioning of the generalized Stokes problem arising from the approximation of the time-dependent Navier-Stokes equations
<|reference_start|>Preconditioning of the generalized Stokes problem arising from the approximation of the time-dependent Navier-Stokes equations: The paper considers standard iterative methods for solving the generalized Stokes problem arising from the time and space approximation of the time-dependent incompressible Navier-Stokes equations. Various preconditioning techniques are considered (Cahouet&Chabard and augmented Lagrangian), and one investigates whether these methods can compete with traditional pressure-correction and velocity-correction methods in terms of CPU time per degree of freedom and per time step. Numerical tests on fine unstructured meshes (68 millions degrees of freedoms) demonstrate convergence rates that are independent of the mesh size and improve with the Reynolds number. Three conclusions are drawn from the paper: (1) Although very good parallel scalability is observed for the augmented Lagrangian method, thorough tests on large problems reveal that the overall CPU time per degree of freedom and per time step is best for the standard Cahouet&Chabar preconditioner. (2) Whether solving the pressure Schur complement problem or solving the full couple system at once does not make any significant difference in term of CPU time per degree of freedom and per time step. (3) All the methods tested in the paper, whether matrix-free or not, are on average 30 times slower than traditional pressure-correction and velocity-correction methods. Hence, although all these methods are very efficient for solving steady state problems, they are not yet competitive for solving time-dependent problems.<|reference_end|>
@article{creff2024preconditioning,
    title={Preconditioning of the generalized Stokes problem arising from the
  approximation of the time-dependent Navier-Stokes equations},
    author={Melvin Creff and Jean-Luc Guermond},
    journal={arXiv preprint arXiv:2407.01783},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01783},
    primaryClass={math.NA cs.NA}
}