GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems
SIAM Journal on Scientific and Statistical Computing
Journal of Computational Physics
Choosing the forcing terms in an inexact Newton method
SIAM Journal on Scientific Computing - Special issue on iterative methods in numerical linear algebra; selected papers from the Colorado conference
A Restricted Additive Schwarz Preconditioner for General Sparse Linear Systems
SIAM Journal on Scientific Computing
First-order system least squares (FOSLS) for coupled fluid-elastic problems
Journal of Computational Physics
Journal of Computational Physics
A Newton method using exact jacobians for solving fluid-structure coupling
Computers and Structures
A Fully Implicit Domain Decomposition Algorithm for Shallow Water Equations on the Cubed-Sphere
SIAM Journal on Scientific Computing
Two-Level Newton and Hybrid Schwarz Preconditioners for Fluid-Structure Interaction
SIAM Journal on Scientific Computing
Parallel Algorithms for Fluid-Structure Interaction Problems in Haemodynamics
SIAM Journal on Scientific Computing
| Hi-index | 31.45 |
Due to the rapid advancement of supercomputing hardware, there is a growing interest in parallel algorithms for modeling the full three-dimensional interaction between the blood flow and the arterial wall. In [4], Barker and Cai developed a parallel framework for solving fluid-structure interaction problems in two dimensions. In this paper, we extend the idea to three dimensions. We introduce and study a parallel scalable domain decomposition method for solving nonlinear monolithically coupled systems arising from the discretization of the coupled system in an arbitrary Lagrangian-Eulerian framework with a fully implicit stabilized finite element method. The investigation focuses on the robustness and parallel scalability of the Newton-Krylov algorithm preconditioned with an overlapping additive Schwarz method. We validate the proposed approach and report the parallel performance for some patient-specific pulmonary artery problems. The algorithm is shown to be scalable with a large number of processors and for problems with millions of unknowns.