Modelling and optimization of photonic crystal fibres
The objective of this research is to develop an accurate and efficient modelling tool for automated optimal design of PCFs. In this thesis, we have studied both electromagnetic (EM) modelling and optimization methodologies. For EM modelling of PCFs, the space filling mode (SFM) - effective index (EI...
Saved in:
| 主要作者: | |
|---|---|
| 其他作者: | |
| 格式: | Theses and Dissertations |
| 出版: |
2008
|
| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/3702 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 機構: | Nanyang Technological University |
| 總結: | The objective of this research is to develop an accurate and efficient modelling tool for automated optimal design of PCFs. In this thesis, we have studied both electromagnetic (EM) modelling and optimization methodologies. For EM modelling of PCFs, the space filling mode (SFM) - effective index (EI) method and the finite element method (FEM) are formulated, implemented, and tested. The SFM-EI is a semi-analytical method and it is efficient, but it can only treat repeated lattice structures. The FEM is a full vectorial numerical tool which can handle complicated geometries. Generally speaking, FEM is accurate, flexible and quite efficient, how- ever, it is still very time-consuming when used for optimal design where hundreds or thousands of individual solutions are needed. For optimization, genetic algorithms (GA) and particle swarm optimization (PSO) are studied, implemented, tested and compared. Both GAsand PSO are population based stochastic optimization tech- niques. GAsmimic the concept of biological genetics and natural evolution while PSO is inspired by social behavior and bird flocking or fish schooling. |
|---|