L’objectif principal de cette thèse est d’étudier d’abord les problèmes potentiels qui peuvent entraver ou accélérer la diffusion des systèmes énergétiques environnementaux (par exemple, énergies renouvelables), puis l’impact de la pénétration de ces technologies dans le marché de l’énergie sur les équilibres du marché. À cette fin, nous observons le processus de diffusion à partir de différents points de vue tels que les décideurs, les fabricants, les clients et l’opérateur d’un système de transmission. Le premier chapitre de cette thèse fournit introduction et le contexte de la recherche. Le deuxième chapitre examine le rôle des décideurs et des fabricants technologiques sur la diffusion de ces technologies, alors que le troisième chapitre est consacré à l’étude de l’impact des différentes dimensions d’information sur la décision d’adopter une technologie environnementale tout au long du processus de telle adoption. Le chapitre quatre examine l’effet de l’entrée de ces technologies (énergies renouvelables) dans le marché d’électricité sur le prix au comptant d’électricité et sur le déséquilibre offre-demande. Enfin, le chapitre cinq conclut les résultats de cette étude et fournit des orientations pour des recherches futures. Les résultats présentés dans cette thèse offrent un aperçu de gestion précieux pour les décideurs, les fabricants de technologie et aux entreprises opérant dans le secteur de l'énergie. / The main goal of this dissertation is to study the potential factors that may hamper or accelerate the diffusion of environmental energy systems (e.g: renewable energies). To this end, using different methods such as game theory, survey data analysis, and time series data analysis we observe the diffusion of these technologies from different perspectives such as policy makers, manufacturers, and customers. This dissertation consists of five chapters. The first chapter provides introduction and background of the research. Second chapter investigates the role of policy makers and technology manufacturers on the diffusion of the environmental energy systems. Chapter three is dedicated to studying the impact of different information dimensions on the different stages of environmental technology adoption process. Chapter four examines effect of the penetration of the environmental energy systems into the electricity market by studying the electricity spot price and electricity supply- demand imbalance. Lastly, chapter five concludes the results of this study and provides directions for future researches. The results reported in this dissertation offer valuable managerial insights to policy makers, technology manufacturers and to firms operating in the energy sector.

La conception d’une boîte de vitesses impose le choix initial d’une architecture, solution de principe de l’objet à concevoir. Ce choix s’avère très structurant et a un impact très fort sur les critères de performance de la boîte de vitesse sans que l’ingénieur ait une visibilité claire sur cet impact. Une fois l’architecture choisie, il est possible par des techniques d’optimisation continue d’optimiser les critères de performance et le respect de contraintes vis-à-vis d’un cahier des charges. Ce genre d’optimisation a pour but de déterminer de manière optimale des dimensions structurantes de la boîte de vitesses comme par exemple les positions des axes de lignes d’arbres dans l’espace ou les diamètres des pignons. L’objectif de cette thèse est d’apporter des techniques scientifiques permettant de choisir l’architecture optimale vis-à-vis de ce cahier des charges. Le développement d’une telle méthode vise à obtenir des boîtes plus performantes, mais également de réduire les temps de développement d’ingénierie en s’assurant par des méthodes scientifiques du respect des contraintes exprimées dans le cahier des charges, et ce dès le choix de l’architecture, chose faite par essais-erreurs dans le cycle de conception habituel. / In the design process of a new gearbox, a choice has to be made between several competing technical solutions referred to as architectures. The purpose of multicriteria architecture optimization is to define the best technical solution in order to meet some given specifications. In the case of automotive gearboxes, the number of technical solutions is so high that it is humanly impossible for an engineer to test every possibility.It is more convenient to computationally generate all the gearbox architectures and select them with an automated analysis of performance and viability for the main criterion. These viable architectures are at this step only described by their kinematic functions, and a second stage considers the discrete spatial positioning of components in order to determine the viable ones for each architecture. Then, the design engineercan compare the viable architectures according to their performance and select the most interesting one.This PhD. propose a novel framework for gearbox synthesis from kinematic specifications and engineering rules and knowledge, using a specific graph as model for gearbox architectures.

Managing large-scale dynamical systems (e.g., transportation systems, complex information systems, and power networks, etc.) in real-time is very challenging considering their complicated system dynamics, intricate network interactions, large scale, and especially the existence of various uncertainties. To address this issue, intelligent techniques which can quickly design decision-making strategies that are robust to uncertainties are needed. This dissertation aims to conquer these challenges by exploring a data-driven decision-making framework, which leverages big-data techniques and scalable uncertainty evaluation approaches to quickly solve optimal control problems. In particular, following techniques have been developed along this direction: 1) system modeling approaches to simplify the system analysis and design procedures for multiple applications; 2) effective simulation and analytical based approaches to efficiently evaluate system performance and design control strategies under uncertainty; and 3) big-data techniques that allow some computations of control strategies to be completed offline. These techniques and tools for analysis, design and control contribute to a wide range of applications including air traffic flow management, complex information systems, and airborne networks.

No description available.

Seeding rate in hard red spring wheat (HRSW) (Triticum aestivum L.) production impacts input cost and grain yield. Predicting the optimal seeding rate (OSR) for HRSW cultivars can aid growers and eliminate the need for costly seeding rate research. Research was conducted to determine the OSR of newer HRSW cultivars (released in 2013 or later) in diverse environments. Nine cultivars with diverse genetic and phenotypic characteristics were evaluated at four seeding rates in 11 environments throughout the northern Great Plains region in 2017-2018. Results from ANOVA indicated environment and cultivar were more important than seeding rate in determining grain yield. Though there was no environment x seeding rate interaction (P=0.37), OSR varied among cultivar within each environment. Cultivar x environment interactions were further explored with the objective of developing a decision support system (DSS) to aid growers in determining the OSR for the cultivar they select, and for the environment in which it is sown. Data from seeding rate trials conducted in ND and MN from 2013-2015 were also used. A novel method for characterizing cultivar for tillering capacity was developed and proposed as a source for information on tillering to be used in statistical modelling. A 10-fold repeated cross-validation of the seeding rate data was analyzed by 10 statistical learning algorithms to determine a model for predicting OSR of newer cultivars. Models were similar in prediction accuracy (P=0.10). The decision tree model was considered the most reliable as bias was minimized by pruning methods, and model variance was acceptable for OSR predictions (RMSE=1.24). Findings from this model were used to develop the grower DSS for determining OSR dependent on cultivar straw strength, tillering capacity, and yield of the environment. Recommendations for OSR ranged from 3.1 to 4.5 million seeds ha-1. Growers can benefit from using this DSS by sowing at OSR relative to their average yields; especially when seeding new HRSW cultivars.

Multiple drive belt conveyors are being increasingly incorporated in mining plans worldwide because of their high economic performance and the ease of moving these installations around, especially in underground mines. A typical modern multi-drive conveyor system consists of one or more intermediate drive stations positioned along the upper stretch of the conveyor and a single drive station situated in the lower stretch. Despite the acknowledged cost saving potential of the multiple drive technology, no previous work was reported on the methodology to realize a cost-effective design of multi-drive belt conveyors. This study investigates a design approach for multiple drive belt conveyors with the objective to achieve the lowest life cycle cost of multi-drive belt conveyors for a specified material transport task. For this purpose, an optimization model for the cost-effective design of multi-drive conveyor systems is formulated on the basis of the recommendations of the DIN 22101 and SANS 1313 standards. For a given number of intermediate drive stations, the proposed model optimizes a set of design parameters so that the minimum equivalent annual cost of a conveyor can be attained whilst handling the transport requirements and design conditions. The conveyor parameters optimized in this study are the rated powers of motors, the rated torques of gear reducers, the diameters and wrap angles of drive pulleys, the belt width, the belt speed, the lengths of the belt sections not nestled between drive pulleys, the spacings between idler rolls and the shell diameters and shaft diameters of idler rolls. For benchmark analysis purposes, a similar optimization model is also developed for the single drive technology. Described as mixed integer nonlinear programming (MINLP) problems, the two optimization models are solved using the MIDACO solver embedded in the MATLAB environment. The results of this study show the validity and effectiveness of the design model proposed for multi-drive belt conveyors. The results also indicate that the multiple drive technology is more beneficial for the conveying over long distances. The impact of the possible instability of inflation throughout the project lifetime is also investigated through three hypothetical scenarios, which involve a fixed inflation rate, a higher fluctuating inflation rate and a lower fluctuating inflation rate, respectively. The results of this sensitivity analysis show that the most cost-effective multi-drive belt conveyors obtained under a fixed inflation rate is robust enough against limited fluctuations of this parameter. / Dissertation (MEng)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

Dans la première partie, on propose une étude sur le problème de nage à faible nombre de Reynolds à partir d'unnageur modélisant la nage des copépodes et du nageur historique de Purcell.En minimisant l’énergie dissipée par les forces de trainée sur le fluide, laquelle est reliée au concept d’efficacitéd’une nage, on utilise les outils géométriques et numériques du contrôle optimal. Le principe du maximum estutilisé pour calculer les contrôles optimaux périodiques satisfaisant une condition de transversalité fine reliée à laminimisation de l’énergie mécanique pour un déplacement fixé où à la maximisation de l’efficacité. Ce sont desproblèmes sous-Riemanniens ce qui permet d’utiliser des techniques efficaces telles que l’approximation nilpotentepour calculer des nages de faible amplitude et qui est utilisée pour calculer des nages sur le vrai système parcontinuation. Les conditions nécessaires et suffisantes du second ordre sont calculées pour sélectionner desminimiseurs faible dans le cas d’une famille de nages périodiques.Dans la seconde partie, on s‘intéresse à la trajectoire d’un engin spatial contrôlé sous l’action d’un champ à forcecentral et où l’on considère les perturbations conservatives dues à l’effet lunaire et à l’aplatissement de la Terre àses pôles. Notre approche est basée sur des techniques moyennisation appliquées sur le système issu du principedu maximum. Nous donnons des résultats de convergence entre le système moyenné et le système non moyenné.Enfin, nous simulons les trajectoires du système non moyennée en utilisant les solutions du système moyennépour initialiser des méthodes numériques indirectes / The first part of this work is devoted to the study of the swimming at low Reynolds number where we consider a2-link swimmer to model the motion of a Copepod and the seminal model of the Purcell Three-link swimmer. Wepropose a geometric and numerical approach using optimal control theory assuming that the motion occursminimizing the energy dissipated by the drag fluid forces related with a concept of efficiency of a stroke. TheMaximum Principle is used to compute periodic controls considered as minimizing control using propertransversality conditions, in relation with periodicity, minimizing the energy dissipated for a fixed displacement ormaximizing the efficiency of a stroke. These problems fall into the framework of sub-Riemannian geometry whichprovides efficient techniques to tackle these problems : the nilpotent approximation is used to compute strokeswith small amplitudes which are continued numerically for the true system. Second order optimality, necessary orsufficient, are presented to select weak minimizers in the framework of periodic optimal controls.In the second part, we study the motion of a controlled spacecraft in a central field taking into account thegravitational interaction of the Moon and the oblateness of the Earth. Our purpose is to study the time minimalorbital transfer problem with low thrust. Due to the small control amplitude, our approach is to define anaveraged system from the Maximum Principle and study the related approximations to the non averaged system.We provide proofs of convergence and give numerical results where we use the averaged system to solve the nonaveraged system using indirect method

Optimal control theory has a long history and broad applications. Motivated by the goal of obtaining insights through unification and taking advantage of the abundant capability to generate data, this thesis introduces some suboptimal schemes via abstract dynamic programming models. As our first contribution, we consider deterministic infinite horizon optimal control problems with nonnegative stage costs. We draw inspiration from the learning model predictive control scheme designed for continuous dynamics and iterative tasks, and propose a rollout algorithm that relies on sampled data generated by some base policy. The proposed algorithm is based on value and policy iteration ideas. It applies to deterministic problems with arbitrary state and control spaces, and arbitrary dynamics. It admits extensions to problems with trajectory constraints, and a multiagent structure. In addition, abstract dynamic programming models are used to analyze $\lambda$-policy iteration with randomization algorithms. In particular, we consider contractive models with infinite policies. We show that well-posedness of the $\lambda$-operator plays a central role in the algorithm. The operator is known to be well-posed for problems with finite states, but our analysis shows that it is also well-defined for the contractive models with infinite states. Similarly, the algorithm we analyze is known to converge for problems with finite policies, but we identify the conditions required to guarantee convergence with probability one when the policy space is infinite regardless of the number of states. Guided by the analysis, we exemplify a data-driven approximated implementation of the algorithm for estimation of optimal costs of constrained linear and nonlinear control problems. Numerical results indicate the potentials of this method in practice. / Teorin om optimal reglering har en lång historia och breda tillämpningsområden.I denna avhandling, som motiveras av att få insikter genom att förena och dra nyttaav den goda möjligheten att generera data, introduceras några suboptimala systemvia abstrakta modeller för dynamisk programmering.I vårt första bidrag betraktar vi ett deterministiskt optimalt regleringsproblemmed oändlig horisont och icke-negativa stegkostnader. Vi hämtar inspiration frånmodellprediktiv reglering med inlärning, som är utformad för system med kontinuerligdynamik och iterativa uppgifter, och föreslår en utrullningsalgoritm som bygger påsamplade data som genereras av en viss baspolicy. Den föreslagna algoritmen byggerpå idéer om värde- och policyiteration. Den är tillämpningsbar för deterministiskaproblem med godtyckliga tillstånds- och kontrollrum samt för system med godtyckligdynamik. Slutligen kan den utvidgas till problem med trajektoriebegränsningar ochen struktur med flera agenter.Dessutom används abstrakta modeller för dynamisk programmering för attanalysera lambdapolicyiteration med randomiseringsalgoritmer. Vi betraktar merspecifikt kontraktiva modeller med oändliga strategier. Vi visar att lambdaoperatorns välbestämdhet spelar en central roll i algoritmen. Det är känt att operatorn ärväldefinierad för problem med ändliga tillstånd, men vår analys visar att den ocksåär väldefinierad för de studerade kontraktiva modellerna med oändliga tillstånd.På samma sätt är det känt att den algoritm vi analyserar konvergerar för problemmed ändliga strategier, men vi identifierar de villkor som krävs för att garanterakonvergens med sannolikhet ett när policyrummet är oändligt, oberoende av antalettillstånd. Med hjälp av analysen exemplifierar vi en datadriven approximativ implementering av algoritmen för uppskattning av optimala kostnader för begränsadelinjära och icke-linjära regleringsproblem. Numeriska resultat visar på potentialen iatt använda denna metod i praktiken. / <p>QC 20211129</p>

The use of solar photovoltaic systems has increased in the past years in an effort to move towards cleaner energy sources. Solar panels are however affected by negative factors such as dust deposition which hinder their performance. The negative effects that dust deposition has on solar panels depend on how much dust gets deposited on solar panels and how it spreads on the top surface. The spread of dust on solar panels can be uniform where all the solar panels in a entire solar photovoltaic array have the same amount of dust deposition. This is an ideal case and can be defined as uniform dust deposition. However, in real life operation, the spread of dust deposition can vary with one solar panel having a different quantity of dust deposition from another. This is defined as non-uniform dust deposition. Non-uniform dust deposition negatively affects the performance of solar panels by reducing the irradiance that reaches the solar cells thereby reducing the performance of the solar panels. The negative effects of non-uniform dust deposition are more significant over time and when there is no intervention to remove the dust. In practice, the negative effects of non-uniform dust deposition on photovoltaic modules has been addressed by periodically cleaning their top surfaces. Periodic cleaning can however increase the operational costs in terms of the cleaning frequency, time taken, cost of cleaning resources and effectiveness. In this study, we propose an optimal cleaning strategy for the solar power plants that are prone to the non-uniform dust deposition. To develop the optimal cleaning strategy, we first investigate the dust deposition process and develop a model to describe the relationship between the solar power generation and non-uniform dust deposition patterns. Then we formulate an optimization model to identify the most cost-effective solar panel cleaning plan. In the optimisation, the additional revenue due to cleaning the solar panels is formulated as the objective function. The decision variables are the number of photovoltaic strings cleaned at each cleaning interval. To highlight the effectiveness of the proposed solar panels cleaning strategy, the developed cleaning strategy is applied to a case study where analysis of the performances of other solar panel cleaning strategies, namely “full cleaning”, “no cleaning” and “random cleaning” is done. The results from the study show that the optimal cleaning strategy outperforms all the other cleaning strategies showing its effectiveness. The optimal cleaning strategy developed is useful to solar photovoltaic plants owners whose plants are located in dusty or polluted areas. It first provides them with an understanding of non-uniform dust deposition. It also provides a way of reducing the effects of non-uniform dust deposition through optimized cleaning which is cost effective and that allows the photovoltaic array to continuously give the desired output. / Dissertation (MEng)--University of Pretoria, 2020. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

The purpose of this study was to investigate the effect of various loads on the force-time characteristics associated with peak power during the hang high pull (HHP). Fourteen athletic men (age: 21.6 ± 1.3 years; height: 179.3 ± 5.6 cm; body mass: 81.5 ± 8.7 kg; 1 repetition maximum [1RM] hang power clean [HPC]: 104.9 ± 15.1 kg) performed sets of the HHP at 30, 45, 65, and 80% of their 1RM HPC. Peak force, peak velocity, peak power, force at peak power, and velocity at peak power were compared between loads. Statistical differences in peak force (p 0.001), peak velocity (p < 0.001), peak power (p 0.015), force at peak power (p < 0.001), and velocity at peak power (p < 0.001) existed, with the greatest values for each variable occurring at 80, 30, 45, 80, and 30% 1RM HPC, respectively. Effect sizes between loads indicated that larger differences in velocity at peak power existed as compared with those displayed by force at peak power. It seems that differences in velocity may contribute to a greater extent to differences in peak power production as compared with force during the HHP. Further investigation of both force and velocity at peak power during weightlifting variations is necessary to provide insight on the contributing factors of power production. Specific load ranges should be prescribed to optimally train the variables associated with power development during the HHP.