Many applications in fluid mechanics require the numerical solution of sequences of linear systems typically issued from finite element discretization of the Navier-Stokes equations. The resulting matrices then exhibit a saddle point structure. To achieve this task, a Newton-based root-finding algorithm is usually employed which in turn necessitates to solve a saddle point system at every Newton iteration. The involved linear systems being large scale and ill-conditioned, effective linear solvers must be implemented. Here, we develop and test several methods for solving the saddle point systems, considering in particular the LU factorization, as direct approach, and the preconditioned generalized minimal residual (ΡGMRES) solver, an iterative approach. We apply the various solvers within the root-finding algorithm for Flow over backward facing step systems. The particularity of Flow over backward facing step system is an interesting case for studying the performance and solution strategy of a turbulence model. In this case, the flow is subjected to a sudden increase of cross-sectional area, resulting in a separation of flow starting at the point of expansion, making the system of differential equations particularly stiff. We assess the performance of the direct and iterative solvers in terms of computational time, numbers of Newton iterations and time steps.
References
[1]
Elman, H.C., Silvester, D.J. and Wathen, A.J. (2011) IFISS: A Computational Laboratory for Investigating Incompressible Flow Problems. SIAM Review, 56, 261-273.
[2]
Elman, H.C., Silvester, D.J. and Wathen, A.J. (2011) Finite Elements and Fast Iterative Solvers: With Applications in Incompressible Fluid Dynamics. Oxford University Press, Oxford.
[3]
Saad, Y. and Schultz, M.H. (1986) The Principle of Minimized Iterations in the Solution of the Matrix Eigenvalue Problem. SIAM Journal on Scientific and Statistical Computing, 7, 856-869.
[4]
Saad, Y. (2003) Iterative Methods for Sparse Linear Systems. SIAM, Philadelphia. https://doi.org/10.1137/1.9780898718003
[5]
Badahmane, A. (2020) Regularized Preconditioned GMRES and the Regularized Iteration Method. Applied Numerical Mathematics, 152, 159-168. https://doi.org/10.1016/j.apnum.2020.01.001
[6]
Arnoldi, W.E. (1951) The Principle of Minimized Iterations in the Solution of the Matrix Eigenvalue Problem. Quarterly of Applied Mathematics, 9, 17-29, https://ci.nii.ac.jp/naid/10020869898/en/ https://doi.org/10.1090/qam/42792
[7]
Gilbert, J.R. and Peierls, T. (2016) Sparse Partial Pivoting in Time Proportional to Arithmetic Operations. SIAM Journal on Scientific and Statistical Computing, 9, 862-874. https://epubs.siam.org/doi/10.1137/0909058 https://doi.org/10.1137/0909058
[8]
Saad, Y. (1996) Preconditioning Techniques. Chap. 10 in Iterative Methods for Sparse Linear Systems. The PWS Series in Computer Science, 9, 862-874.
[9]
Amestoy, P.R., Duff, I.S., L’Excellent, J.-Y. and Koster, J. (2001) A Fully Asynchronous Multifrontal Solver Using Distributed Dynamic Scheduling. SIAM Journal on Matrix Analysis and Applications, 23, 15-41. https://doi.org/10.1137/S0895479899358194