Browsing by Author "Cilliers, Pierre"
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- ItemEnhanced method of moments performance through efficient implementation and error estimation(Stellenbosch : Stellenbosch University, 2023-03) Cilliers, Pierre; Botha, Matthys; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.ENGLISH ABSTRACT: The emergence of increasingly ambitious engineering projects has pushed the limits of traditional computational electromagnetics (CEM) simulation software. This has necessitated the development and implementation of new methods that can effectively utilise the available hardware resources of modern computing systems. Due to the increasing availability of large cluster computing systems, research into increasing the capabilities of electromagnetic (EM) field solvers in terms of performance and scale of the problems they can handle has mainly focused on distributed memory parallelisation schemes. The single-core performance of modern processors has however also continued to improve. New processors are also continually being released with increasingly large core counts. Thus designing implementations of solvers that can effectively utilise the available hardware on shared memory systems is crucial for application to both small and very large problems. To this end, this document presents the development and implementation of a numerical field solver that is designed to push the limits of single central processing unit (CPU) performance for the Method of Moments (MoM). This solver forms the core of a software library, the electromagnetic kernel library (EMK), that aims to provide utilities for performing numerical field simulations as well as for developing methods for large-scale antenna analysis. This solver is compared to the solver in a commercial CEM software suite, to assess its performance and accuracy. As well as efficiently computing numerical solutions, it is also important to verify the validity of such solutions and improve them by locating and eliminating sources of error. This is done through a process of error estimation, which seeks to measure the extent to which a numerical solution deviates from the true solution. To this end, a goal-oriented a posteriori error estimator is formulated for MoM-based EM analysis. Goal-oriented error estimators allow for the solution error to be determined with regard to a particular quantity of interest (QOI). This estimator is used in the computation of multi-port Zparameters by driving a simple mesh refinement algorithm, to assess its effectiveness in generating efficient meshes in terms of degrees of freedom
- ItemExploring South Africa’s southern frontier : a 20-year vision for polar research through the South African National Antarctic Programme(Academy of Science of South Africa, 2017) Ansorge, Isabelle J.; Skelton, Paul; Bekker, Annie; de Bruyn, P.J. Nico; Butterworth, Doug; Cilliers, Pierre; Cooper, John; Cowan, Don A.; Dorrington, Rosemary; Fawcett, Sarah; Fietz, Susanne; Findlay, Ken P.; Froneman, William P.; Grantham, Geoff H.; Greve, Michelle; Hedding, David; Hofmeyr, Greg G. J.; Kosch, Michael; Le Roux, Peter C.; Lucas, Mike; MacHutchon, Keith; Meiklejohn, Ian; Nel, Werner; Pistorius, Pierre; Ryan, Peter G.; Stander, Johan; Swart, Sebastiaan; Treasure, Anne; Vichi, Marcello; Van Vuuren, Bettine J.No abstract available
- ItemA spatio-temporal framework for modelling informal settlement growth(Stellenbosch : Stellenbosch University, 2021-12) Cilliers, Pierre; Van Vuuren, Jan Harm; Van Heerden, Quintin; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.ENGLISH ABSTRACT: Many developing countries grapple with the problem of rapid informal settlement emergence and expansion. This exacts considerable costs from neighbouring urban areas, largely as a result of environmental, sustainability and health-related problems associated with such settlements, which can threaten the local economy. Hence, there is a need to understand the nature of, and to be able to predict, informal settlement emergence locations as well as the rate and extent of such settlement expansion in developing countries. Although an abundance of research has been dedicated to developing computerised mathematical models for predicting future informal settlement expansion, there are no models in the literature for successfully predicting future informal settlement emergence and expansion which employ the considerable power of machine learning in a temporal setting. In this dissertation, a novel generic framework is proposed for machine learning-inspired prediction of future spatio-temporal informal settlement population growth. This data-driven framework comprises three functional components which facilitate informal settlement emergence and growth modelling within a user- specified area. The framework outputs are based on a computed set of influential spatial feature predictors pertaining to the area in question. The objective of the framework is ultimately to identify those spatial and other factors that in- fluence the location, formation and growth rate of an informal settlement most significantly, by applying a machine learning modelling approach to multiple data sets related to the households and spatial attributes associated with informal settlements. Based on the aforementioned influ- encing factors, a cellular automaton transition rule is developed, enabling the spatio-temporal modelling of the rate and extent of future formations and expansions of informal settlements. Furthermore, the framework facilitates a flexible, exploratory analysis of model results in com- bination with existing structured informal settlement expansion data in order to gain actionable insights into their management. Two separate instantiations of this framework are implemented on a personal computer as concept demonstrations. The first is applied to a real-world case study related to a densely populated informal settlement area of a South African municipality in order to illustrate the practical applicability of the proposed framework. The second implementation is aimed at comparing the model performance of the proposed framework with that of an existing model in the literature on the same real-world case study area.