Preconditioners for Linearized Discrete Compressible Euler Equations

We consider a Newton-Krylov approach for discretized compressible Euler equations. A good preconditioner in the Krylov subspace method is essential for obtaining an efficient solver in such an approach. In this paper we compare point-block-Gauss-Seidel, point-block-ILU and point-block-SPAI preconditioners. It turns out that the SPAI method is...

Fast Iterative Solution of the Time-Harmonic Elastic Wave Equation at Multiple Frequencies

Seismic Full-Waveform Inversion is an imaging technique to better understand the earth's subsurface. Therefore, the reflection intensity of sound waves is measured in a field experiment and is matched with the results from a computer simulation in a least-squares sense. From a computational point-of-view, but also from an economic view point,...

Induced Dimension Reduction algorithms for solving non-symmetric sparse matrix problems

In several applications in science and engineering, different types of matrix problems emerge from the discretization of partial differential equations.<br/>This thesis is devoted to the development of new algorithms to solve this<br/>kind of problems. In particular, when the matrices involved are sparse and<br/>non-symmetric. The new algorithms...

Mathematical formulations and algorithms for fast and robust power system simulations

During the normal operation, control and planning of the power system, grid operators employ numerous tools including the Power Flow (PF) and the Optimal Power Flow (OPF) computations to keep the balance in the power system. The solution of the PF computation is used to assess whether the power system can function properly for the given...

Exploiting BiCGstab(?) strategies to induce dimension reduction

IDR(s) [P. Sonneveld and M. B. van Gijzen, SIAM J. Sci. Comput., 31 (2008), pp. 1035–1062] and BiCGstab(?) [G. L. G. Sleijpen and D. R. Fokkema, Electron. Trans. Numer. Anal., 1 (1993), pp. 11–32] are two of the most efficient short-recurrence iterative methods for solving large nonsymmetric linear systems of equations. Which of the two is best...

On the Convergence Behavior of IDR(s) and Related Methods

An explanation is given of the convergence behavior of IDR(s) methods. The convergence mechanism of these algorithms has two components. The first consists of damping properties of certain factors in the residual polynomials, which becomes less important for large values of s. The second component depends on the behavior of Lanczos polynomials...

Induced Dimension Reduction Method for Solving Linear Matrix Equations

This paper discusses the solution of large-scale linear matrix equations using the Induced Dimension reduction method (IDR(s)). IDR(s) was originally presented to solve system of linear equations, and is based on the IDR(s) theorem. We generalize the IDR(s) theorem to solve linear problems in any finite-dimensional space. This generalization...

Linear power flow method improved with numerical analysis techniques applied to a very large network

In this paper, we propose a fast linear power flow method using a constant impedance load model to simulate both the entire Low Voltage (LV) and Medium Voltage (MV) networks in a single simulation. Accuracy and efficiency of this linear approach are validated by comparing it with the Newton power flow algorithm and a commercial network design...

Fourier Analysis of Iterative Methods for the Helmholtz Problem

This thesis attempts to explain the convergence behaviour of solving Helmholtz problem by investigating its spectral properties. Fourier analysis is employ to solve the eigenvalues of the matrices that are involved in the iterative methods. The numerical experiment is conducted to verify the conclusions by Fourier analysis and also to reveal...

Acceleration of the 2D Helmholtz model HARES

Exploiting the flexibility of IDR(s) for grid computing

The IDR(s) method that is proposed in [26] is an efficient limited memory method for solving large nonsymmetric systems of linear equations. In [11] an IDR(s) variant is described that has a single synchronisation point per iteration step, which makes this variant well-suited for parallel and grid computing. In this paper, we combine this IDR(s)...

On preconditioning incompressible non-Newtonian flow problems

This paper deals with fast and reliable numerical solution methods for the incompressible non-Newtonian Navier-Stokes equations. To handle the nonlinearity of the governing equations, the Picard and Newton methods are used to linearize these coupled partial differential equations. For space discretization we use the finite element method and...

Some numerical aspects for solving sparse large linear systems derived from the Helmholtz equation

In this report, several numerical aspects and diculties for solving a linear system derived from the time-harmonic wave equations are overviewed. The presentation begins with the derivation of the governing equation for waves propagating in general inhomogeneous media. Due to the need of numerical solutions, various discretizations based on...

On the convergence behaviour of IDR(s)

An explanation is given of the convergence behaviour of the IDR(s) methods. The convergence of the IDR(s) algorithms has two components. The first consists of damping properties of certain factors in the residual polynomials, which becomes less important for large values of s. The second component depends on the behaviour of quasi-Lanczos...

Flexible and multi-shift induced dimension reduction algorithms for solving large sparse linear systems

We give two important generalizations of the Induced Dimension Reduction (IDR) approach for the solution of linear systems. We derive a flexible and a multi-shift Quasi-Minimal Residual IDR (QMRIDR) variant. Numerical examples are presented to show the effectiveness of these new IDR variants compared to existing ones and to other Krylov subspace...

Induced Dimension Reduction method for solving linear matrix equations

This paper discusses the solution of large-scale linear matrix equations using the Induced Dimension reduction method (IDR(s)). IDR(s) was originally presented to solve system of linear equations, and is based on the IDR(s) theorem. We generalize the IDR(s) theorem to solve linear problems in any finite-dimensional space. This generalization...

An elegant IDR(s) variant that efficiently exploits bi-orthogonality properties

The IDR(s) method that is proposed in [18] is a very efficient limited memory method for solving large nonsymmetric systems of linear equations. IDR(s) is based on the induced dimension reduction theorem, that provides a way to construct subsequent residuals that lie in a sequence of shrinking subspaces. The IDR(s) algorithm that is given in [18...