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The work, of which this thesis is a record, is concerned with the development of a microprocessor based system to simulate, at a speed approaching real time, the steady state and transient response of induction motor drives under various conditions. This development proceeds by outlining the basic theory involved in the simulation of induction motors. This is followed by an analysis of the various integration methods available for the solution of ordinary differential equations. This analysis being primarily concerned with determining the most efficient means of solving the set of equations used to describe induction motor response. The third part of this work explains the development of a distributed processing system that was used to achieve the simulation. This part of the work starts by describing the procedures that were followed in the development of a single processor-co-processor system and continues to detail the extension from one microprocessor to four in order to increase the speed of solution.

The quest for energy is leading the industry into drilling deeper wells. Typically, a temperature gradient of 1°C/150 ft can be expected, with bottom hole temperatures reaching beyond 200°C in many areas of the world. Moreover, the increased recovery benefits and cost reductions possible with the use of horizontal and multilateral wells has triggered a need for higher power energy conversion systems in bottom hole assemblies, such as rotary steerable tools and downhole tractors. The concepts developed throughout this work address some of these new needs. This research investigated improvements, novel solutions and considerations that will lead to significant advantages in terms of reliability, extended temperature operation, increased power capability and reduced size and cost of compact harsh environment energy conversion systems. Improvements to both the electromechanical subsystem and the power electronic subsystem are introduced. Air gap viscous losses were shown to a have a significant effect on the optimal design of submersible PM (permanent magnet) machines, and a design procedure to account for this loss component in the design was developed. The application of a dual winding exterior rotor PM machine in a downhole environment enabled a significant increase in the application’s torque capability, provided protection against generator winding over voltage, and reduced parts count. Comprehensive switching device qualification, testing, and simulation lead to a simple failure mitigation technique for the operation of the most suitable devices at elevated temperature. A flying capacitor multilevel inverter was then successfully constructed and temperature tested. A novel motor drive concept suited for elevated temperature oil filled environment applications concluded the research.

No description available.

No description available.

Thesis (MEng)--University of Stellenbosch, 2000. / ENGLISH ABSTRACT: The design of an effective layout for a facility plays an important roll in its successful operation. The centralisation of certain activities has advantages in that it can reduce inventory levels, simplify material management, ease standardisation and provide better control over the quality of the final product. This thesis identifies the factors that must be taken into account in order to obtain a layout that functions effectively. Different layout types, material handling concepts and flow patterns are investigated. A variety of tools to analyse and evaluate these factors are discussed. A logical, practical and simple process is discussed which addresses the planning of the facility layout in a systematic manner. The volume of components that have to be rebuilt by the facility is obtained by making use of existing maintenance plans. The labour requirement is determined based on this volume. Activities and support services within the facility are compared to each other in order to determine the importance of the relationship between each. This relationship plays an important role in the placement of cells relative to each other. The layout is adjusted by taking the practical limitations of the current facility into account. A few alternative layouts are developed and they are rated against a list of parameters in order to obtain the most suitable layout. The processes and floor layout of the current facility are investigated and discussed. Aspects such as material, equipment, cleaning of components and the flow of documentation and information are discussed. Finally the way of operating in the future is discussed. This is obtained by looking at the theoretically determined layout and adapting it by taking both the good and bad points of the current layout into consideration. / AFRIKAANSE OPSOMMING: Die ontwerp van 'n effektiewe uitleg vir 'n fasiliteit speel 'n belangrike rol in die suksesvolle werking daarvan. Die sentralisering van sekere aktiwiteite het voordele deurdat dit voorraadvlakke kan verlaag, beheer van voorraad vereenvoudig, standaardisering vergemaklik en beter beheer oor die gehalte van die finale produk bied. Hierdie tesis identifiseer die faktore wat in berekening gebring moet word ten einde 'n uitleg te verkry wat effektief funksioneer. Verskillende tipes uitlegte, materiaal hanterings konsepte en vloei patrone word ondersoek. 'n Verskeidenheid hulpmiddels om hierdie faktore te ontleed en te evalueer word bespreek. 'n Logiese, praktiese en eenvoudige proses word bespreek wat die beplanning van die fasiliteits uitleg sistematies aanpak. Deur van beskikbare instandhoudings planne gebruik te maak word die volume van komponente wat deur die fasiliteit herbou moet word bepaal. Die mannekrag behoeftes word bepaal gebaseer op hierdie volume. Aktiwiteite en ondersteunings funksies binne die fasiliteit word vergelyk ten einde die belangrikheid van die verhoudings tussen elkeen te bepaal. Hierdie verhoudings speel 'n belangrike rol in die plasing van selle relatief tot mekaar. Deur die praktiese beperkings van die huidige fasiliteit in ag te neem word die uitleg aangepas. 'n Paar alternatiewe uitlegte word ontwikkel en evalueer teen 'n lys parameters om die mees geskikte uitleg te verkry. Die prosesse en vloeruitleg van die huidige uitleg word ondersoek en bespreek. Hieronder word aspekte soos toerusting, materiaal, skoonmaak van komponente en die vloei van dokumentasie en inligting gedek. Ten slotte word gekyk na die toekomstige werkswyse wat gevolg gaan word. Dit word bereik deur die teoreties bepaalde uitleg te neem en aan te pas deur sommige van die goeie en slegte punte van die huidige uitleg in ag te neem.

Thesis (D.Tech.: Mechanical Engineering)-Dept. of Mechanical Engineering, Durban Institute of Technology, 2005 xxi, 265 leaves / The aim of this study is to develop, using neural networks, a model to aid the performance monitoring of operational diesel engines in industrial settings. Feed-forward and modular neural network-based models are created for the prediction of the specific fuel consumption on any normally aspirated direct injection four-stroke diesel engine. The predictive capability of each model is compared to that of a published quadratic method. Since engine performance maps are difficult and time consuming to develop, there is a general scarcity of these maps, thereby limiting the effectiveness of any engine monitoring program that aims to manage the fuel consumption of an operational engine. Current methods applied for engine consumption prediction are either too complex or fail to account for specific engine characteristics that could make engine fuel consumption monitoring simple and general in application. This study addresses these issues by providing a neural network-based predictive model that requires two measured operational parameters: the engine speed and torque, and five known engine parameters. The five parameters are: rated power, rated and minimum specific fuel consumption bore and stroke. The neural networks are trained using the performance maps of eight commercially available diesel engines, with one entire map being held out of sample for assessment of model generalisation performance and application validation. The model inputs are defined using the domain expertise approach to neural network input specification. This approach requires a thorough review of the operational and design parameters affecting engine fuel consumption performance and the development of specific parameters that both scale and normalize engine performance for comparative purposes. Network architecture and learning rate parameters are optimized using a genetic algorithm-based global search method together with a locally adaptive learning algorithm for weight optimization. Network training errors are statistically verified and the neural network test responses are validation tested using both white and black box validation principles. The validation tests are constructed to enable assessment of the confidence that can be associated with the model for its intended purpose. Comparison of the modular network with the feed-forward network indicates that they learn the underlying function differently, with the modular network displaying improved generalisation on the test data set. Both networks demonstrate improved predictive performance over the published quadratic method. The modular network is the only model accepted as verified and validated for application implementation. The significance of this work is that fuel consumption monitoring can be effectively applied to operational diesel engines using a neural network-based model, the consequence of which is improved long term energy efficiency. Further, a methodology is demonstrated for the development and validation testing of modular neural networks for diesel engine performance prediction.

Thesis (D.Tech.: Mechanical Engineering)-Dept. of Mechanical Engineering, Durban Institute of Technology, 2005 xxi, 265 leaves / The aim of this study is to develop, using neural networks, a model to aid the performance monitoring of operational diesel engines in industrial settings. Feed-forward and modular neural network-based models are created for the prediction of the specific fuel consumption on any normally aspirated direct injection four-stroke diesel engine. The predictive capability of each model is compared to that of a published quadratic method. Since engine performance maps are difficult and time consuming to develop, there is a general scarcity of these maps, thereby limiting the effectiveness of any engine monitoring program that aims to manage the fuel consumption of an operational engine. Current methods applied for engine consumption prediction are either too complex or fail to account for specific engine characteristics that could make engine fuel consumption monitoring simple and general in application. This study addresses these issues by providing a neural network-based predictive model that requires two measured operational parameters: the engine speed and torque, and five known engine parameters. The five parameters are: rated power, rated and minimum specific fuel consumption bore and stroke. The neural networks are trained using the performance maps of eight commercially available diesel engines, with one entire map being held out of sample for assessment of model generalisation performance and application validation. The model inputs are defined using the domain expertise approach to neural network input specification. This approach requires a thorough review of the operational and design parameters affecting engine fuel consumption performance and the development of specific parameters that both scale and normalize engine performance for comparative purposes. Network architecture and learning rate parameters are optimized using a genetic algorithm-based global search method together with a locally adaptive learning algorithm for weight optimization. Network training errors are statistically verified and the neural network test responses are validation tested using both white and black box validation principles. The validation tests are constructed to enable assessment of the confidence that can be associated with the model for its intended purpose. Comparison of the modular network with the feed-forward network indicates that they learn the underlying function differently, with the modular network displaying improved generalisation on the test data set. Both networks demonstrate improved predictive performance over the published quadratic method. The modular network is the only model accepted as verified and validated for application implementation. The significance of this work is that fuel consumption monitoring can be effectively applied to operational diesel engines using a neural network-based model, the consequence of which is improved long term energy efficiency. Further, a methodology is demonstrated for the development and validation testing of modular neural networks for diesel engine performance prediction.

Plenty of designing works focused on railway technology were made through the time. But the majority of them are concerned with high-speed trains or at least express train locomotives, but works devoted to design freight or shunting locomotives can be barely found. This diploma thesis is looking into nearly unexplored territory and finding out, which possibilities in the design of shunting locomotives of independent traction do exist, and find better solutions then those currently used, where possible and appropriate. The result of this master thesis work is design of shunting locomotive, which respects the requirements of railway standards, ergonomics and safety, and still brings in modern design solutions.