Comparative study of inner-outer Krylov solvers for linear systems in structured and high-order unstructured CFD problems
<|reference_start|>Comparative study of inner-outer Krylov solvers for linear systems in structured and high-order unstructured CFD problems: Advanced Krylov subspace methods are investigated for the solution of large sparse linear systems arising from stiff adjoint-based aerodynamic shape optimization problems. A special attention is paid to the flexible inner-outer GMRES strategy combined with most relevant preconditioning and deflation techniques. The choice of this specific class of Krylov solvers for challenging problems is based on its outstanding convergence properties. Typically in our implementation the efficiency of the preconditioner is enhanced with a domain decomposition method with overlapping. However, maintaining the performance of the preconditioner may be challenging since scalability and efficiency of a preconditioning technique are properties often antagonistic to each other. In this paper we demonstrate how flexible inner-outer Krylov methods are able to overcome this critical issue. A numerical study is performed considering either a Finite Volume (FV), or a high-order Discontinuous Galerkin (DG) discretization which affect the arithmetic intensity and memory-bandwith of the algebraic operations. We consider test cases of transonic turbulent flows with RANS modelling over the two-dimensional supercritical OAT15A airfoil and the three-dimensional ONERA M6 wing. Benefits in terms of robustness and convergence compared to standard GMRES solvers are obtained. Strong scalability analysis shows satisfactory results. Based on these representative problems a discussion of the recommended numerical practices is proposed.<|reference_end|>
@article{jadoui2024comparative,
    title={Comparative study of inner-outer Krylov solvers for linear systems in
  structured and high-order unstructured CFD problems},
    author={Mehdi Jadoui and Christophe Blondeau and Emeric Martin and Florent
  Renac and Franc{c}ois-Xavier Roux},
    journal={Computers and Fluids, Volume 244, 15 August 2022, 105575},
    year={2024},
    doi={10.1016/j.compfluid.2022.105575},
    archivePrefix={arXiv},
    eprint={2404.17870},
    primaryClass={math.NA cs.NA}
}