Modern electromagnetic scattering

We develop a numerically stable algorithm for electromagnetic wave propagation through planar stratified media. This algorithm is implemented in a modern programming language and is suitable for the study of such applications as Anderson localization and perfect lensing. Our algorithm remains numerically stable even in the presence of large absorption. Furthermore, in the context of the linear response laws and causality, we analyze a vanishing absorption approximation, which is commonly used in wave scattering problems. We show that it is easy to violate causality in the frequency-domain by making the vanishing absorption approximation. We also develop an orders-of-scattering approximation, termed "screened cylindrical void/core" (SCV) approximation, for wave scattering from a large host cylinder containing N eccentrically embedded core cylinders. The SCV approximation is developed via separation of variables and a cluster T-matrix. We establish the limitations of the SCV approximation and it is in good agreement with the numerically-exact solution. Furthermore, we illustrate that the large host cylinder model with N cylindrical inclusions can be used to theoretically and experimentally investigate strong multiple scattering effects in random media, such as Anderson localization.

Name thesis:degree_name

Doctor of Philosophy (Ph.D.)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Physics

Grantor dc:publisher

Colorado School of Mines. Arthur Lakes Library

Year dc:date.issued

2013

Author dc:creator

• Yuffa, Alex J.

Advisor dc:contributor.advisor

• Scales, John Alan

Committee members dc:contributor.committeemember

• Martin, P. A.
• Wood, David M.
• Kowalski, Frank V.
• Taylor, P. C. (Philip Craig), 1942-

• localization
• left handed material
• causality
• electromagnetic scattering
• cluster T-matrix

Statement dc:rights

• Copyright of the original work is retained by the author.

Language dc:language.iso

eng, English

Identifier

T 7329

OAI identifier oai:identifier

oai:repository.mines.edu:11124/79528