Hybrid Krylov methods for nonlinear systems of equations
SIAM Journal on Scientific and Statistical Computing
Journal of Computational Physics
Iterative solution methods
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
Analysis of Projection Methods for Solving Linear Systems with Multiple Right-Hand Sides
SIAM Journal on Scientific Computing
NITSOL: A Newton Iterative Solver for Nonlinear Systems
SIAM Journal on Scientific Computing
A nonlinear multigrid method for the three-dimensional incompressible Navier-Stokes equations
Journal of Computational Physics
SIAM Journal on Scientific Computing
SIAM Journal on Matrix Analysis and Applications
A parallel solver for unsteady incompressible 3D Navier—Stokes equations
Parallel Computing - Special issue on parallel computing in aerospace
Dynamic Iteration Using Reduced Order Models: A Method for Simulation of Large Scale Modular Systems
SIAM Journal on Numerical Analysis
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
A New Look at Proper Orthogonal Decomposition
SIAM Journal on Numerical Analysis
Jacobian-free Newton-Krylov methods: a survey of approaches and applications
Journal of Computational Physics
GREMLINS: a large sparse linear solver for grid environment
Parallel Computing
On choosing a nonlinear initial iterate for solving the 2-D 3-T heat conduction equations
Journal of Computational Physics
Fractal boundaries of basin of attraction of Newton-Raphson method in helicopter trim
Computers & Mathematics with Applications
Advances in Engineering Software
Advances in Engineering Software
Hi-index | 31.45 |
Contemporary time stepping schemes applied to the solution of unsteady nonlinear fluid flow problems are considered. The iterative solution of arising series of linear and nonlinear systems and the choice of the initial guess are addressed. The computation of a better initial guess for two iterative linear system solvers (GCR and GMRES) is based on the history of the evolution problem solving. For implicitly discretized nonlinear evolution problems, a reduced model technique is developed for computing a better initial guess for the inexact Newton method. The computational effect of the chosen initial guess is compared with that of the standard (physically motivated) initial guess.