Catálogo de publicaciones - libros

In nonlinear electromagnetic field computations, one is not only faced with large jumps of material coefficients across material interfaces but also with high variation in these coefficients even inside homogeneous materials due to the nonlinearity. The radiation condition can conveniently be taken into account by a coupled boundary integral and domain integral variational formulation. The coupled finite and boundary element discretization leads to large-scale nonlinear algebraic systems. In this paper we propose special inexact Newton methods where the Jacobi systems arising in every step of the Newton method are solved by a special preconditioned finite and boundary element tearing and interconnecting solver. The numerical experiments show that the preconditioner proposed in the paper can handle large jumps in the coefficients across the material interfaces as well as high variation in these coef- ficients on the subdomains. Furthermore, the convergence does not deteriorate if many inner subdomains touch the unbounded exterior subdomain.

In this paper, a numerical algorithm solving large sparse linear systems that arise in electromagnetic field computation will be presented. It is based on hierarchical partitioning of the matrix and uses block-wise low-rank approximation in combination with element dropping in order to construct a preconditioner for iterative solution. Within the BE-FE coupling, this approximate factorisation is applied as preconditioner for the FE system. The treatment of multiply connected domains will also be described. The efficiency of the presented solver will be shown by means of an electromagnetic valve.

We investigate the solution of linear systems that appear when model reduction via system balancing is applied in circuit simulation and design. We show how the properties and structure of the coefficient matrices allow the development and use of efficient parallel algorithms for the solution of the corresponding linear systems. Experimental results are reported on an Intel SMP multiprocessor.

We present a method for converting 1-D Maxwell equation into a linear system using the Multivariable Output Error State Space (MOESP) method, a subspace system identification method. To show the efficiency of the method, we first apply it to a set of ordinary differantial equations. Input and output from the equation set are computed by numerical methods and the obtained data is used for building the required matrices. An appropriate Single Input Single Output (SISO) linear system is estimated by MOESP algorithm for the equation at hand. The goal of the research is to build a low order linear state space system model for the Maxwell equation. On the other hand the order estimation for the system can be used in other way. For example, with this estimation one can determine an appropriate order for the physical system, for which one of the well-known model order reduction techniques can be used to obtain a reduced order model.

Stochastic differential algebraic equations (SDAEs) arise as a mathematical model for electrical network equations that are influenced by additional sources of Gaussian white noise. In this paper we discuss adaptive linear multi-step methods for the numerical integration of SDAEs, in particular stochastic analogues of the trapezoidal rule and the two-step backward differentiation formula, together with a new step-size control strategy. Test results illustrate the performance of the presented methods.

Grid technologies offer powerful computing resources for all domains, but the highly heterogeneous and dynamic nature of the Grids needs adaptable, scalable and extensible scheduling systems. In this paper we describe a dynamic, centralized scheduling mechanism based on Master-Worker paradigm for efficient execution of a set of loosely coupled tasks in a Grid environment. This mechanism offers high programmability features, adaptability and reliability towards processor failure. Experiments are presented that demonstrate the effectiveness of our approach.