For gas turbines, the demand for high-performance, more efficient and longer-life turbine blades is increasing. This is especially so, now that there is a need for high-power and low-weight aircraft gas turbines. Thus, the search for improved design methodologies for the optimisation of combustor exit temperature profiles enjoys high priority. Traditional experimental methods are found to be too time-consuming and costly, and they do not always achieve near-optimal designs. In addition to the above deficiencies, methods based on semi-empirical correlations are found to be lacking in performing three-dimensional analyses and these methods cannot be used for parametric design optimisation. Computational fluid dynamics has established itself as a viable alternative to reduce the amount of experimentation needed, resulting in a reduction in the time scales and costs of the design process. Furthermore, computational fluid dynamics provides more insight into the flow process, which is not available through experimentation only. However, the fact remains that, because of the trial-and-error nature of adjusting the parameters of the traditional optimisation techniques used in this field, the designs reached cannot be called “optimum”. The trial-and-error process depends a great deal on the skill and experience of the designer. Also, the above technologies inhibit the improvement of the gas turbine power output by limiting the highest exit temperature possible, putting more pressure on turbine blade cooling technologies. This limitation to technology can be overcome by implementing a search algorithm capable of finding optimal design parameters. Such an algorithm will perform an optimum search prior to computational fluid dynamics analysis and rig testing. In this thesis, an efficient methodology is proposed for the design optimisation of a gas turbine combustor exit temperature profile. The methodology involves the combination of computational fluid dynamics with a gradient-based mathematical optimiser, using successive objective and constraint function approximations (Dynamic-Q) to obtain the optimum design. The methodology is tested on three cases, namely: (a) The first case involves the optimisation of the combustor exit temperature profile with two design variables related to the dilution holes, which is a common procedure. The combustor exit temperature profile was optimised, and the pattern factor improved, but pressure drop was very high. (b) The second case involves the optimisation of the combustor exit temperature profile with four design variables, one equality constraint and one inequality constraint based on pressure loss. The combustor exit temperature profile was also optimised within the constraints of pressure. Both the combustor exit temperature profile and pattern factor were improved. (c) The third case involves the optimisation of the combustor exit temperature profile with five design variables. The swirler angle and primary hole parameters were included in order to allow for the effect of the central toroidal recirculation zone on the combustor exit temperature profile. Pressure loss was also constrained to a certain maximum. The three cases show that a relatively recent mathematical optimiser (Dynamic-Q), combined with computational fluid dynamics, can be considered a strong alternative to the design optimisation of a gas turbine combustor exit temperature profile. This is due to the fact that the proposed methodology provides designs that can be called near-optimal, when compared with that yielded by traditional methods and computational fluid dynamics alone. / Thesis (PhD)--University of Pretoria, 2009. / Mechanical and Aeronautical Engineering / unrestricted

In the continuous casting of steel, the Submerged Entry Nozzle (SEN), in particular the SEN geometry, has a primary influence on the flow pattern: the SEN controls the speed, direction and other characteristics of the jet entering the mould. The SEN is however relatively inexpensive to change (in comparison with other continuous casting equipment). Thus; there is a feasible incentive to exactly understand and predict the flow of molten steel through the SEN and into the mould, in order to maximise the quality of the steel by altering the design of the SEN. By changing the SEN geometry and SEN design, the flow pattern in the mould will also change: it is thus possible to obtain an optimum SEN design if (or when) the desired flow patterns and/or certain predetermined temperature distributions are achieved. Expensive and risky plant trials were traditionally utilised to “perfect” continuous casting processes. As opposed to the plant trials, this dissertation is concerned with the Computational Fluid Dynamics (CFD) modelling of the SEN and mould, which, when used in conjunction with the Mathematical Optimiser LS-OPT, will enable the optimisation of the SEN design to achieve desired results. The CFD models are experimentally verified and validated using 40%-scaled (designed and built in-house) and full-scale water model tests. This dissertation proves that the CFD modelling of the SEN and mould can be quite useful for optimisation and parametric studies, especially when automated model generation (geometry, mesh and solution procedures) is utilised. The importance of obtaining reliable and physically correct CFD results is also emphasised; hence the need for CFD model verification using water modelling. / Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2007. / Mechanical and Aeronautical Engineering / unrestricted

In modern heat transfer, shape and geometric optimisation are new considerations in the evaluation of thermal performance. In this research, we employed constructal theory and design to present three-dimensional theoretical and numerical solutions of conjugate forced convection heat transfer in heat generating devices with cooling channels of different cross-sectional shapes. In recent times, geometric configurations of cooling channel have been found to play an important role in thermal performance. Therefore, an efficient ways of optimally designing these cooling channels shapes is required. Experimentation has been extensively used in the past to understand the behaviour of heat removals from devices. In this research, the shapes of the cooling channels and the configurations of heat-generating devices were analytically and numerically studied to minimise thermal resistance and thus illustrate cooling performance under various design conditions. The cooling channels of five different cross-sectional shapes were studied: Circular, square, rectangular, isosceles right triangular and equilateral triangular. They were uniformly packed and arranged to form larger constructs. The theoretical analysis is presented and developed using the intersection of asymptotes method. This proves the existence of an optimal geometry of parallel channels of different cross-sectional shapes that penetrate and cool a volume with uniformly distributed internal heat generation and heat flux, thus minimising the global thermal resistance. A three-dimensional finite volume-based numerical model was used to analyse the heat transfer characteristics of the cross-sectional shapes of various cooling channels. The numerical computational fluid dynamics (CFD) package recently provided a more cost-effective and less time-consuming means of achieving the same objective. However, in order to achieve optimal design solutions using CFD, the thermal designers have to be well experienced and carry out a number of trial-and-error simulations. Unfortunately, this can not always guarantee an accurate optimal design solution. In this thesis a mathematical optimisation algorithm (a leapfrog optimisation program and DYNAMIC-Q algorithm) coupled with numerical CFD was employed and incorporated into the finite volume solver, –FLUENT, and grid (geometry and mesh) generation package, – GAMBIT to search and identify the optimal design variables at which the system would perform optimally for greater efficiency and better accuracy. The algorithm was also specifically designed to handle constraint problems where the objective and constraint functions were expensive to evaluate. The automated process was applied to different design cases of cooling channels shapes. These cooling channels were embedded in a highly conductive solid and the peak temperature was minimised. The trend and performance of all the cooling channel shapes cases studied were compared analytically and numerically. It was concluded that an optimal design can be achieved with a combination of CFD and mathematical optimisation. Furthermore, a geometric optimisation of cooling channels in the forced convection of a vascularised material (with a localised self-cooling property subjected to a heat flux) was also considered. A square configuration was studied with different porosities. Analytical and numerical solutions were provided. This gradient-based optimisation algorithm coupled with CFD was used to determine numerically the optimal geometry that gave the lowest thermal resistance. This optimiser adequately handled the numerical objective function obtained from numerical simulations of the fluid flow and heat transfer. The numerical results obtained were in good agreement with results obtained in the approximate solutions based on scale analyses at optimal geometry dimensions. The approximate dimensionless global thermal resistance predicted the trend obtained in the numerical results. This shows that there were unique optimal design variables (geometries) for a given applied dimensionless pressure number for fixed porosity. The results also showed that the material property had a significant influence on the performance of the cooling channel. Therefore, when designing the cooling structure of vascularised material, the internal and external geometries of the structure, material properties and pump power requirements would be very important parameters to be considered in achieving efficient and optimal designs for the best performance. Finally, this research investigated a three-dimensional geometric optimisation of conjugate cooling channels in forced convection with an internal heat generation within the solid for an array of cooling channels. Three different flow orientations based on constructal theory were studied numerically- firstly, an array of channels with parallel flow; secondly, an array of channels in which flow of every second row was in a counter direction and finally, an array of channels in which the flow direction in every channel was opposite to that of previous channel. The geometric configurations and flow orientations were optimised in such a way that the peak temperature was minimised subject to the constraint of fixed global volume of solid material. The optimisation algorithm coupled with CFD was also used to determine numerically the optimal geometry that gave the lowest thermal resistance. The use of the optimisation algorithm coupled with the computational fluid dynamics package; render the numerical results more robust with respect to the selection of optimal structure geometries, internal configurations of the flow channels and dimensionless pressure difference. / Thesis (PhD(Eng))--University of Pretoria, 2012. / Mechanical and Aeronautical Engineering / unrestricted

Background: Optimal scheduling is a common practice in various industries, facili-tating efficient workflow management. Accelerating the generation of schedules while maintaining their optimality could encourage broader adoption of this approach inindustry settings. Previous work has aimed to estimate the makespan for the JobShop Scheduling Problem, showing promising results. Objectives: Given the increasing demand for AI and Machine Learning (ML) solutions across industries, this research aims to explore the integration of ML techniquesinto optimal scheduling processes. Specifically, the goal is to develop a faster scheduling solution without compromising the optimality of the generated schedules. The proposed approach combines the effectiveness and speed of ML with the optimal results obtained from mathematical scheduling models. Methods: This thesis focuses on the Job Shop Scheduling (JSS) Problem, where a mathematical scheduler is tasked with minimizing the makespan of a set of jobs while following a predefined set of rules. An initial investigation is performed to establish if there is potential in providing the scheduler with its optimal makespan to decrease the scheduling time. To generalize the application of the concept, the study investigates the potential efficiency acceleration achieved by providing the scheduler with a Machine Learning estimated makespan. This involves training a Neural Network(NN) to estimate the optimal makespan of job sets, which is then utilized to speedup the scheduling process. Results: The preliminary investigation demonstrates that providing the scheduler with the optimal makespan results in an average speed-up of schedule generationby 24%. The results of the scheduling time with the NN estimated makespan is on the other hand not as well performing. Despite achieving a level of accuracy in estimating the makespan, the resulting speed-up in the scheduler’s performance falls short. For the scheduler to benefit from being provided an estimated makespan it is therefore theorized to require a close-to-perfect estimation of the makespan, which was not achieved with the trained NN model. The trained NN reached an average accuracy of 95.75%. Conclusions: The study concludes that while ML models can accurately estimate makespan, the observed speed-up in scheduling performance is not as significant as anticipated. The correlation between well-estimated makespan and speed-up appearsto be inconsistent, indicating potential limitations in the current approach. Further investigation into the search algorithm employed by the scheduling tool Gurobi mayprovide insights into optimizing the scheduling process more effectively. In summary, while the integration of ML techniques shows promise in accelerating scheduling processes, a higher accuracy of the ML model would be required. Additional researchis needed to refine the approach and potentially bring a faster optimal scheduling solution into the future. / Bakgrund: Optimal schemaläggning är en vanlig implemetation inom flera olika branscher och underlättar hantering och effektiviserar arbetsflöden. Att påskynda genereringen av scheman samtidigt som den optimala aspekten av schemaläggning inte går till spillo, skulle kunna främja en bredare användning av optimal schemaläggning för fler brancher. Tidigare undersökningar har gjorts för att estimera "makespan" för Job Shop problemet inom schemaläggning och har visat lovande resultat. Syfte: Med den ökande efterfrågan på AI- och maskininlärnings lösningar inom olika branscher syftar denna forskning till att utforska integrationen av ML-tekniker i den optimala schemaläggningsprocessen. Målet är att utveckla en snabbare schemaläggningslösning utan att kompromissa med det genererade schemats optimalitet. Det föreslagna tillvägagångssättet kombinerar ML’s effektivitet och hastighet med de optimala resultaten som den matematiska schemaläggningsmodellen erbjuder. Metod: Forskningen fokuserar på problemet med schemaläggning för jobbshoppen(JSSP), där en matematisk schemaläggare har i uppgift att minimera makespan fören uppsättning jobb med hänsyn till ett par fördefinierade regler. En initial under-sökning görs, vilket visar att det finns potential i att tillhandahålla schemaläggarendess optimala makespan för att minska schemaläggningstiden. För att generalisera tillämpningen undersöker studien den potentiella accelerationen som uppnås genomatt tillhandahålla schemaläggaren ett maskininlärt uppskattat makespan. Detta medför att träna ett neuralt nätverk för att uppskatta det optimala makespanet för en mängd jobbuppsättningar, som sedan används för att påskynda schemaläggningsprocessen. Resultat: Den preliminära undersökningen visar att schemaläggaren resulterar i igenomsnittlig hastighetsökning av schemagenereringen med cirka 24% när den får tillgång till det optimala makespanet för de givna jobben. Resultaten av schemaläggningstiden med det neurala nätverkets uppskattade makespan är dock lägre än förväntat. Trots att en viss noggrannhetsnivå uppnås vid estimeringen av makespanet, når den resulterande hastighetsökningen i schemaläggarens prestanda inte upp tillförväntningarna. För att schemaläggaren ska dra nytta av att tillhandahålla ett uppskattad makespan krävs en nära perfekt uppskattning av makespan, vilket inte uppnåddes med det tränade neurala nätverket. Slutsatser: Studien drar slutsatsen att även om ML-modeller kan uppskatta makespan någorlunda noggrant, är den observerade hastighetsökningen i schemaläggningen inte lika betydande som förväntat. Korrelationen mellan väl uppskattad makespan och hastighetsökning verkar vara inkonsekvent, vilket indikerar potentiella begränsningar i det nuvarande tillvägagångssättet. Vidare undersökning av sökalgoritmen som används av schemaläggningsverktyget Gurobi kan ge insikter för att optimera schemaläggningsprocessen mer effektivt. Sammanfattningsvis visar integrationen av ML-tekniker lovande resultat för att accelerera schemaläggningsprocesser, men en bättre estimering av makespan skulle krävas. Ytterligare forskning behövs för att förbättra tillvägagångssättet och potentiellt introducera en snabbare optimal schemaläggningslösning för framtiden.

Les deux dernières décennies ont été marquées par une augmentation significative des données collectées dans les systèmes d'informations. Cette masse de données contient des informations riches et peu exploitées. Cette réalité s’applique au secteur de la santé où l'informatisation est un enjeu pour l’amélioration de la qualité des soins. Les méthodes existantes dans les domaines de l'extraction de processus, de l'exploration de données et de la modélisation mathématique ne parviennent pas à gérer des données aussi hétérogènes et volumineuses que celles de la santé. Notre objectif est de développer une méthodologie complète pour transformer des données de santé brutes en modèles de simulation des parcours de soins cliniques. Nous introduisons d'abord un cadre mathématique dédié à la découverte de modèles décrivant les parcours de soin, en combinant optimisation combinatoire et Process Mining. Ensuite, nous enrichissons ce modèle par l’utilisation conjointe d’un algorithme d’alignement de séquences et de techniques classiques de Data Mining. Notre approche est capable de gérer des données bruitées et de grande taille. Enfin, nous proposons une procédure pour la conversion automatique d'un modèle descriptif des parcours de soins en un modèle de simulation dynamique. Après validation, le modèle obtenu est exécuté pour effectuer des analyses de sensibilité et évaluer de nouveaux scénarios. Un cas d’étude sur les maladies cardiovasculaires est présenté, avec l’utilisation de la base nationale des hospitalisations entre 2006 et 2015. La méthodologie présentée dans cette thèse est réutilisable dans d'autres aires thérapeutiques et sur d'autres sources de données de santé. / During the last two decades, the amount of data collected in Information Systems has drastically increased. This large amount of data is highly valuable. This reality applies to health-care where the computerization is still an ongoing process. Existing methods from the fields of process mining, data mining and mathematical modeling cannot handle large-sized and variable event logs. Our goal is to develop an extensive methodology to turn health data from event logs into simulation models of clinical pathways. We first introduce a mathematical framework to discover optimal process models. Our approach shows the benefits of combining combinatorial optimization and process mining techniques. Then, we enrich the discovered model with additional data from the log. An innovative combination of a sequence alignment algorithm and of classical data mining techniques is used to analyse path choices within long-term clinical pathways. The approach is suitable for noisy and large logs. Finally, we propose an automatic procedure to convert static models of clinical pathways into dynamic simulation models. The resulting models perform sensitivity analyses to quantify the impact of determinant factors on several key performance indicators related to care processes. They are also used to evaluate what-if scenarios. The presented methodology was proven to be highly reusable on various medical fields and on any source of event logs. Using the national French database of all the hospital events from 2006 to 2015, an extensive case study on cardiovascular diseases is presented to show the efficiency of the proposed framework.

The purpose of this study is to perform an investigation of the numerical methods that may contribute to the design and analysis of liquid containers. The study examines several of these methods individually, namely Computational Fluid Dynamics (CFD) analysis of sloshing and Finite Element Methods (FEM) analysis of impact, to evaluate their contribution to the design cycle. Techniques that enhance the use of the various methods are presented and examined to demonstrate effectiveness. In the case of sloshing analysis, experimental tests performed add to the understanding of the phenomena at hand and qualifies the validity of the numerical method used (CFD). As a final contribution, the study presents a method of utilising impact analysis tools, FEM, and CFD in a Multidisciplinary Design Optimisation (MDO) environment. This is an introductory attempt at demonstrating a single coupled multidisciplinary method of designing liquid containers. The results of the study demonstrate a number of valuable numerical techniques that may be used in the design of liquid containers. The presented Total Deviation Value (TDV) proves to be an effective single quantification of sloshing performance and the CFD tools used to determine the value demonstrate sufficient ability to reproduce the sloshing event itself. More advanced experimental facilities would provide a more in-depth understanding of the limitations of the CFD analysis. The use of numerical optimisation adds a valuable dimension to the use of numerical simulations. Significant design improvements are possible for several design variables without performing exhaustive studies and provide interesting information about design trends. Finally, the use of multiple disciplines, FEM and CFD, in conjunction with the available numerical optimisation routines offers a powerful multidisciplinary design tool that can be adapted to any base geometry and is capable of finding optimal trade offs between the two disciplines according to the designer’s needs. This study provides a platform for further investigations in the use and coupling of sloshing and impact analysis in the design of industrial liquid container applications. / Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2006. / Mechanical and Aeronautical Engineering / unrestricted