Browsing by Author "Vermeulen, Alexander Alfons"
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- ItemThe design of a dual reflector feed using surrogate modeling techniques(Stellenbosch : Stellenbosch University, 2016-03) Vermeulen, Alexander Alfons; De Villiers, D.; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.ENGLISH ABSTRACT: The optimisation of a feed horn for a dual Gregorian reflector antenna system using surrogate modelling was investigated. This included a brief overview of dual reflector antenna systems as well as their performance parameters. The design for a three axial choke horn with a variety of matching sections was described for use with the optimisation. Two techniques were investigated, namely space mapping (SM) and a Kriging interpolate based approach. The SM technique consisted of augmenting a fast coarse model by aligning it to a slow fine model. This showed potential but was ultimately hampered by the lack of a coarse model that did not require a full wave simulation for the primary feed pattern and so was abandoned. The interpolation based technique could use two approaches. The first consisted of an interpolate based on a coarse data set that was then corrected using a regression model based on the difference between the fine and coarse model at a few training sits. The second approach consisted of only an interpolate that was based on a fine data set. The technique was applied to a multi-objective optimisation (MOO) problem. The optimisation aimed at minimizing the reflection coeficient and maximizing the sensitivity of the reflector system. It was shown to work well and produced reasonably accurate results while reducing the total optimisation time from potentially weeks or months down to the order of a day. As part of the investigation an MOO algorithm called multi-objective population based incremental learning (MOPBIL) was implemented. The basic concepts of MOO and MOPBIL were discussed and the implementation was described. This implementation was also fully tested and shown to approximate the Pareto front well.