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https://hdl.handle.net/2440/92233
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Type: | Conference paper |
Title: | MATNEC: an in-house developed tool for electromagnetic simulation and evolutionary optimization of wire antennas |
Author: | Zhao, S. Fumeaux, C. Fickenscher, T. |
Citation: | 2014 International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications, NEMO 2014, 2014, pp.1-4 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Issue Date: | 2014 |
ISBN: | 9781479928200 |
Conference Name: | Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications, NEMO 2014 (NEMO 2014) (14 May 2014 - 16 May 2014 : Pavia, Italy) |
Statement of Responsibility: | S. Zhao, C. Fumeaux, and T. Fickenscher |
Abstract: | Various techniques of numerical electromagnetic computation have been widely used for decades in the design of antennas. In particular, the Numerical Electromagnetic Code was developed based on the method of moments to solve integral equations for current distributions, and thus, it is particularly efficient for the modeling and analysis of wire antennas and other metal structures. The computation load can, however, increase significantly with the increase of complexity in structure modeling. Radial basis functions are employed for geometry description to ease such burden by representing structural variations with a minimal number of variables. Evolutionary optimizations are used to explore a wider solution space in a timely manner in the pursuit of near-optimal solution in a multivariable environment. An in-house tool called MATNEC integrating modeling, simulation, analysis, and optimization is developed, which can successfully deliver near-optimal solutions under a pre-defined fitness function, considering both antenna efficiency and bandwidth. Helical antennas with variations in radius and pitch are successfully optimized, which effectively verifies the robustness of MATNEC and methodology applied |
Keywords: | NEC; Method of Moments; Evoluntionary optimization; Genetic algorithm; Particle swarm algorithm; Small antennas |
Rights: | © 2014 IEEE |
DOI: | 10.1109/NEMO.2014.6995661 |
Appears in Collections: | Aurora harvest 7 Electrical and Electronic Engineering publications |
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