Computational electromagnetics

About: Computational electromagnetics is a research topic. Over the lifetime, 6412 publications have been published within this topic receiving 113727 citations. The topic is also known as: Electromagnetic field analysis.

Multidisciplinary shape optimization in aerodynamics and electromagnetics using genetic algorithms

Abstract: SUMMARY A multiobjective multidisciplinary design optimization (MDO) of two-dimensional airfoil is presented. In this paper, an approximation for the Pareto set of optimal solutions is obtained by using a genetic algorithm (GA). The first objective function is the drag coefficient. As a constraint it is required that the lift coefficient is above a given value. The CFD analysis solver is based on the finite volume discretization of the inviscid Euler equations. The second objective function is equivalent to the integral of the transverse magnetic radar cross section (RCS) over a given sector. The computational electromagnetics (CEM) wave field analysis requires the solution of a two-dimensional Helmholtz equation which is obtained using a fictitious domain method. Numerical experiments illustrate the above evolutionary methodology on an IBM SP2 parallel computer. Copyright © 1999 John Wiley & Sons, Ltd.

Introduction of Frequency-Dependent Line Parameters into an Electromagnetic Transients Program

Abstract: A new method is given for digitally modeling the frequency dependence of the line parameters for an overhead transmission line. Using Fourier transforms, the method models only the line characteristics, leaving the terminations to be chosen by the user. The model of the frequency-dependent line is embedded in a general purpose electromagnetic transients program providing the user with a wide choice of termination and excitation conditions. The method promises to be accurate, and provides insight into the propagation of the aerial and ground modes of transmission. A comparison of line modeling using the method of characteristics, ?equivalents, and frequency-dependent line parameters is given. Although the frequency dependence has been implemented for only one two-phase line, the method is general in principle.

Computational electromagnetics

Abstract: Computational electromagnetics (CEM) is a natural extension of the analytical approach to solving the Maxwell equations [Elliott 1966]. This scientific discipline is based on numerically solving the governing partial differential or integral equations derived from first principles. In spite of a fundamental difference in representing the solution either in the continuum or in the discretized space, both approaches satisfy all pertaining theorems rigorously. Although numerical solutions of CEM generate only a point value for a specific simulation, the complexity of physics and of the field configuration are no longer the limiting factors as they are to the analytical approach. With the advent of high-performance computing systems, CEM is becoming the mainstay for engineering applications. Numerical simulation technology is the most cost-effective means of meeting many technical challenges in the areas of electromagnetic signature processing, antenna design, biomedical application, electromagnetic coupling, microwave device design and assembly. In fact, the military applications in radar signature reduction and integrated broadband communication system design have become the cutting edge of this relatively new technology. The technical transitions have already favorably influenced commercial ventures in telecommunications, magnetically levitated transportation systems, microwave data link optimization, and mobile antenna design, as well as microcircuit packaging. All computational electromagnetics methods also have limitations in their ability to duplicate physics in high fidelity. For a specific application, the numerical accuracy requirement has a direct connection to the computational efficiency. The inaccuracy incurred by a numerical simulation is attributable to the mathematical model of the physics, the numerical algorithm, and the computational accuracy. For example, in the electromagnetic signature simulations, the scattering-field formulation eliminates completely the quasiphysical error involved in the incident wave when it propagates from the far field boundary to the scatter. This accuracy advantage over the total-field formulation is significant by invoking the equivalent field theorem to accomplish the nearto far-field transformation. The computational accuracy is controlled by the algorithm and computing system adopted. Error induced by the discretization consists of the roundoff and the truncation error. The roundoff error is contributed by the computing system and is problem-size-dependent. Because this error behavior is random, it is the most difficult to evaluate. One anticipates that this type of error will be a concern for solving procedures involving large-scale matrix manipulation such as the method of moments [Harrington 1968] and the implicit numerical algorithm for finite-difference or finite-volume methods. The truncation error for time-dependent calculations appears as dissipation and dispersion, which can be assessed and alleviated by mesh system refinements. Finally, the numerical error can be the consequence of a specific numerical formulation. The error becomes pronounced when a special phenomenon is investigated or when a discontinuous and distinctive

A Domain Decomposition Method With Nonconformal Meshes for Finite Periodic and Semi-Periodic Structures

Abstract: We present a domain decomposition method as a preconditioner for Krylov-type solvers to model complex electromagnetic problems containing periodicities. The method reduces memory requirements by decomposing the original problem into several nonoverlapping sub-domains. The 1st order transmission condition is employed on interfaces between adjacent sub-domains to enforce continuity of electromagnetic fields and to ensure the sub-domain problems are well-posed. By following the spirit of duality paring a symmetric system is obtained. To reduce the computational burdens of the present method, the finite element tearing and interconnecting like algorithm is adopted. This algorithm results in the computation of the so-called "numerical" Green's function, which can be compressed efficiently via a rank-revealing matrix factorization algorithm. The final system matrix is solved by Krylov solvers instead of classical stationary solvers. To improve the convergence of iterative solvers, several robust implementation details are discussed and the choice of some popular Krylov-subspace solvers is studied through numerical examples.