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dc.contributor.advisorLu, Junwei
dc.contributor.advisorLewis, Andrew
dc.contributor.authorIreland, David John
dc.date.accessioned2018-01-23T02:17:05Z
dc.date.available2018-01-23T02:17:05Z
dc.date.issued2010
dc.identifier.doi10.25904/1912/1499
dc.identifier.urihttp://hdl.handle.net/10072/365312
dc.description.abstractAntennas utilising a dielectric medium are technologies that have become popular in modern wireless platforms. They offer several desirable features such as high efficiency, electrically small and resistance to proximity detuning. Being a volumetric radiator however, realising a final, commercially competitive solution, often requires the use of a computational optimisation algorithm. In the realm of antenna design the practice of optimisation typically involves an automated routine consisting of a heuristic algorithm and a forward solving engine such as the finite element method (FEM) or finite difference time domain (FDTD) method. The solving engine is used to derive a post-processed performance value typically referred to as an objective or fitness function, while the heuristic method uses the objective function data to determine the next trial solution or solutions that approach a design goal. Nowadays, commercially viable antenna platforms are not characterised by a single performance value, but rather, a series of objective functions that are often inherently conflicting. Thus, an increase in one objective function results in a decrease in another. The optimisation algorithm is therefore required to seek a solution dictated by the preferences of the designer. Classical literature dominantly featured preference articulation, a priori, where the set of objectives are transformed into a scalar using a predefined preference arrangement. Contemporary theory implements the articulation a posteriori, where the complete set of compromise solutions are sought by the optimisation algorithm. It is hypothesised that modern multi-objective optimisation (MOO) theory, using a posteriori preference articulation, can be more useful for contemporary antenna design. By treating the objectives as individual dimensions in a mathematical space, it allows for independent, simultaneous optimisation. At the time of writing this dissertation, all commercial simulation software that include an optimisation algorithm use a predefined preference to the performance criteria. Thus, where a large set of equally potential solutions exist, only one final solution is delivered. This thesis examines two novel dielectric antenna technologies and uses modern MOO theory to obtain new solutions that supersede their prototypes. Taking a commercial perspective by optimising the electromagnetic performance and the physical size of the antenna simultaneously, it is hypothesised this allows an unprecedented insight into the inherent tradeoffs of practical antenna configurations.
dc.languageEnglish
dc.publisherGriffith University
dc.publisher.placeBrisbane
dc.rights.copyrightThe author owns the copyright in this thesis, unless stated otherwise.
dc.subject.keywordsmulti-optimisation
dc.subject.keywordsMOD
dc.subject.keywordsdielectric antenna technology
dc.subject.keywordsfinite element method
dc.subject.keywordsFEM
dc.subject.keywordsfinite difference time domain method
dc.subject.keywordsFDTD
dc.titleDielectric Antennas and Their Realisation Using a Pareto Dominance Multi-Objective Particle Swarm Optimisation Algorithm
dc.typeGriffith thesis
gro.facultyScience, Environment, Engineering and Technology
gro.rights.copyrightThe author owns the copyright in this thesis, unless stated otherwise.
gro.hasfulltextFull Text
dc.contributor.otheradvisorThiel, David
dc.rights.accessRightsPublic
gro.identifier.gurtIDgu1324524700390
gro.source.ADTshelfnoADT0
gro.source.GURTshelfnoGURT1048
gro.thesis.degreelevelThesis (PhD Doctorate)
gro.thesis.degreeprogramDoctor of Philosophy (PhD)
gro.departmentGriffith School of Engineering
gro.griffith.authorIreland, David J.


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