Some Applications of Vector Fitting in the Solution of Electromagnetic Fields and Interactions

Speaker:    Evan Richards

 Date:          Wed  January 29, 2014

Time:         6:00 Refreshments and snacks
                  6:30 to 8:00 Seminar including Q&A period

Location:  University of Maryland
                 2460 A. V. Williams Building (ECE Conference Room)
                 (Directions)

Abstract

A common engineering task is deducing an equivalent circuit that has electrical characteristics that match those of a system being analyzed. In some cases, the system may only be known through a series of hardware tests or simulations, limiting knowledge to the discretely sampled data points that have been measured. In other scenarios, there may already exist a numerical recipe for predicting the behavior of the system under analysis, but the process would be much faster if there were a theoretical equation that could be evaluated directly. In these situations, the equivalent electrical circuit, and thus circuit theory provide an efficient means for numerically analyzing the system. The key objective is then to estimate the circuit parameters that form the deterministic model of the system.

Vector Fitting (VF) is a suitable method for obtaining a circuit-based model (or macromodel) for a system. Specifically for material measurement applications, VF is shown to estimate either the permittivity or permeability of a multi-Debye material accurately, even when measured in the presence of noise and interferences caused by test setup imperfections.

A brief history and survey of methods utilizing VF for material measurement will be introduced in this work. It is shown how VF is useful for macromodeling dielectric materials after being measured with standard transmission line and freespace methods. The sources of error in both an admittance tunnel test device and stripline resonant cavity test device are identified and VF is employed for correcting these errors. Fullwave simulations are performed to model the test setup imperfections and the sources of interference they cause are further verified in actual hardware measurements. An accurate macromodel is attained as long as the signal-to-interference-ratio (SIR) in the measurement is sufficiently high such that the Debye relaxations are observable in the data.

Finally, VF is applied for macromodeling the time history of the total fields scattering from a perfectly conducting wedge. This effort is an initial test to see if a time domain theory of diffraction exists, and if the diffraction coefficients may be exactly modeled with VF. This section concludes how VF is not only useful for applications in material, but for the solution of modeling fields and interactions in general.

Bio

 Evan Richards received a B.S. degree in 2011 and M.S. degree in 2013, both in electrical engineering, from Arizona State University (ASU).  His research interests at ASU have involved analyzing, automating, and designing improvements for microwave material measurement devices, methods of inverting material properties, and analyzing magneto-dielectric antennas.

Workshop on modeling Optical Nanoantennas and RF devices

Speaker:    Aditya Kalavagunta

 Date:         Thursday June 12, 2014

Time:         5:00 to 5:30 Pizza, Refreshments, and snacks
                  5:30 to 6:00 Seminar including Q&A period
                  6:00 to 7:00
Location:  University of Maryland
                 2460 A. V. Williams Building (ECE Conference Room)
                 (Directions)

Abstract

COMSOL will present a workshop on modeling Optical Nanoantennas and RF devices using their general-purpose software platform that is based on advanced numerical methods for modeling and simulating physics-based problems.

Bio
Aditya Kalavagunta is a Technical Sales Engineer with COMSOL. He received his Ph.D. (Electrical engineering) in Semiconductor and Optical Physics from Vanderbilt University in 2009. He has over 10 years  experience in the simulation of semiconductor and optoelectronics devices. At COMSOL he has helped a variety of customers with applications including heat transfer, electromagnetics and semiconductor physics. He has been an avid COMSOL user since 2003.

Novel and Effective Preconditioners for Iterative Solvers

 Date:         Thursday June 12, 2014

Time:         5:00 to 5:30 Pizza, Refreshments, and snacks
                  5:30 to 6:00 Distinguish Lecture on Novel and Effective Preconditioners for Iterative Solvers
                  6:00 to 7:00 Q&A
Location:  University of Maryland
                 2460 A. V. Williams Building (ECE Conference Room)
                 (Directions)

Abstract

 Solutions of extremely large matrix equations require iterative solvers. MLFMA accelerates the matrix-vector multiplications performed with every iteration. Despite the acceleration provided by MLFMA, the number of iterations should also be kept at a minimum, especially if the dimension of the matrix is in the order of millions. This is exactly where the preconditioners are needed. We have developed several novel preconditioners that can be used to accelerate the solution of various problems formulated with different types of integral equations. For example, it is well known that the electric-field integral equation (EFIE) is worse conditioned than the magnetic-field integral equation (MFIE) for conductor problems. Therefore, the preconditioners that we develop for EFIE are crucial for the solution of extremely large EFIE problems. For dielectric problems, we formulate several different types of integral equations to investigate which ones have better conditioning properties. Furthermore, we develop effective preconditioners specifically for dielectric problems. In this talk, we will review three classes of preconditioners:
1. Sparse near-field preconditioners
2. Approximate full-matrix preconditioners
3. Schur complement preconditioning for dielectric problems
We will present our efforts to devise effective preconditioners for MLFMA solutions of difficult electromagnetics problems involving both conductors and dielectrics, such as the block-diagonal preconditioner (BDP), incomplete LU (ILU) preconditioners, sparse approximate inverse (SAI) preconditioners, iterative near-field (INF) preconditioner, approximate MLFMA (AMLFMA) preconditioner, the approximate Schur preconditioner (ASP), and the iterative Schur preconditioner (ISP)..

Bio