No description available.

Die Schwingfestigkeit einer Welle oder Achsen unterliegt einer Streuung. Diese Streuung wird durch Werkstoff- und Fertigungseinflüsse verursacht. Die Kenntnis der Streuung ist essenziell zur Vermeidung von Schadensfällen. Bislang besteht die Problematik, dass seitens der Schwingfestigkeitsstreuung im Bereich der Langzeitfestigkeit nur wenige Literaturwerte vorliegen und eine experimentelle Bestimmung mit geringem Fehler äußerst zeit- und kostenaufwendig ist. Zudem erfassen die beiden genannten Optionen, aufgrund der immer zugrunde liegenden experimentellen Ermittlung der Werte der Schwingfestigkeitsstreuung, nicht alle für die auszulegende Welle oder Achse möglichen Streueinflüsse. Daher ist das Ziel dieser Arbeit, eine Methode zur Abschätzung der Streuung der Schwingfestigkeit für Vergütungsstähle zu entwickeln. Für die Entwicklung einer solchen Methode gilt es, die im Kontext einer sogenannten zweifachen Wöhlerlinie auftretenden Schadensorte – Bauteiloberfläche und Bauteilinneres – im Hinblick auf die ursächlichen Schädigungsmechanismen detailliert zu betrachten. Auf den identifizierten Einflussgrößen der Schädigungsmechanismen aufbauend werden geeignete Konzepte zur Schwingfestigkeitsabschätzung für die Schädigungsmechanismen entwickelt, die die Einflussgrößen erfassen. Dabei wird zur Vorhersage der Schwingfestigkeit beim Schädigungsmechanismus ausgehend von der Bauteiloberfläche ein Kerbspannungskonzept und für den Schädigungsmechanismus ausgehend vom Bauteilinneren ein bruchmechanisches Konzept genutzt. Auf diesen aufbauend wird ein stochastisches Modell der Langzeitfestigkeit entwickelt. Dieses wird anhand durchgeführter experimenteller Untersuchungen sowie im Kontext bestehender Literaturdaten bewertet. / The fatigue strength of a shaft or axles is subjected to a scatter. This scatter is caused by material and manufacturing influences. Knowledge of the scatter is essential for avoiding failures. Up to now, the problem has been that only a few literature values are available on fatigue-strength scatter in the range of long-term strength and an experimental determination with a small error is extremely time-consuming and expensive. In addition, the two options mentioned, due to the experimental determination of the values of the fatigue-strength scatter, which is always the basis, do not include all possible scatter influences for the shaft or axle to be designed. Therefore, the aim of this work is to develop a method for estimating the scatter of the fatigue strength for quenched and tempered steels. For the development of such a method it is necessary to consider in detail the failure locations occurring in the context of a so-called duplex S-N curve - component surface and component interior - with regard to the causal failure mechanisms. Based on the identified influencing parameters of the failure mechanisms, suitable concepts for fatigue-strength estimation are developed for the failure mechanisms that capture the influencing parameters. A notch-stress concept is used to predict the fatigue strength for the failure mechanism originating from the component surface, and a fracture-mechanics concept is used for the failure mechanism originating from the interior of the component. Based on these, a stochastic model of the long-term strength is developed. This model is evaluated on the basis of experimental investigations and in the context of existing literature data.

No description available.

The subprime mortgage crisis (SMC) is an ongoing housing and nancial crisis that was triggered by a marked increase in mortgage delinquencies and foreclosures in the U.S. It has had major adverse consequences for banks and nancial markets around the globe since it became apparent in 2007. In our research, we examine an originator's (OR's) nonlinear stochastic optimal control problem related to choices regarding deposit inflow rates and marketable securities allocation. Here, the primary aim is to minimize liquidity risk, more speci cally, funding and credit crunch risk. In this regard, we consider two reference processes, namely, the deposit reference process and the residential mortgage loan (RML) reference process. This enables us to specify optimal deposit inflows as well as optimal marketable securities allocation by using actuarial cost methods to establish an ideal level of subprime RML extension. In our research, relationships are established in order to construct a stochastic continuous-time banking model to determine a solution for this optimal control problem which is driven by geometric Brownian motion. In this regard, the main issues to be addressed in this dissertation are discussed in Chapters 2 and 3. In Chapter 2, we investigate uncertain banking behavior. In this regard, we consider continuous-time stochastic models for OR's assets, liabilities, capital, balance sheet as well as its reference processes and give a description of their dynamics for each stochastic model as well as the dynamics of OR's stylized balance sheet. In this chapter, we consider RML and deposit reference processes which will serve as leading indicators in order to establish a desirable level of subprime RMLs to be extended at the end of the risk horizon. Chapter 3 states the main results that pertain to the role of stochastic optimal control in OR's risk management in Theorem 2.5.1 and Corollary 2.5.2. Prior to the stochastic control problem, we discuss an OR's risk factors, the stochastic dynamics of marketable securities as well as the RML nancing spread method regarding an OR. Optimal portfolio choices are made regarding deposit and marketable securities inflow rates given by Theorem 3.4.1 in order to obtain the ideal RML extension level. We construct the stochastic continuoustime model to determine a solution for this optimal control problem to obtain the optimal marketable securities allocation and deposit inflow rate to ensure OR's stability and security. According to this, a spread method of RML financing is imposed with an existence condition given by Lemma 3.3.2. A numerical example is given in Section 3.5 to illustrates the main issues raised in our research. / Thesis (M.Sc. (Applied Mathematics))--North-West University, Potchefstroom Campus, 2011.

The subprime mortgage crisis (SMC) is an ongoing housing and nancial crisis that was triggered by a marked increase in mortgage delinquencies and foreclosures in the U.S. It has had major adverse consequences for banks and nancial markets around the globe since it became apparent in 2007. In our research, we examine an originator's (OR's) nonlinear stochastic optimal control problem related to choices regarding deposit inflow rates and marketable securities allocation. Here, the primary aim is to minimize liquidity risk, more speci cally, funding and credit crunch risk. In this regard, we consider two reference processes, namely, the deposit reference process and the residential mortgage loan (RML) reference process. This enables us to specify optimal deposit inflows as well as optimal marketable securities allocation by using actuarial cost methods to establish an ideal level of subprime RML extension. In our research, relationships are established in order to construct a stochastic continuous-time banking model to determine a solution for this optimal control problem which is driven by geometric Brownian motion. In this regard, the main issues to be addressed in this dissertation are discussed in Chapters 2 and 3. In Chapter 2, we investigate uncertain banking behavior. In this regard, we consider continuous-time stochastic models for OR's assets, liabilities, capital, balance sheet as well as its reference processes and give a description of their dynamics for each stochastic model as well as the dynamics of OR's stylized balance sheet. In this chapter, we consider RML and deposit reference processes which will serve as leading indicators in order to establish a desirable level of subprime RMLs to be extended at the end of the risk horizon. Chapter 3 states the main results that pertain to the role of stochastic optimal control in OR's risk management in Theorem 2.5.1 and Corollary 2.5.2. Prior to the stochastic control problem, we discuss an OR's risk factors, the stochastic dynamics of marketable securities as well as the RML nancing spread method regarding an OR. Optimal portfolio choices are made regarding deposit and marketable securities inflow rates given by Theorem 3.4.1 in order to obtain the ideal RML extension level. We construct the stochastic continuoustime model to determine a solution for this optimal control problem to obtain the optimal marketable securities allocation and deposit inflow rate to ensure OR's stability and security. According to this, a spread method of RML financing is imposed with an existence condition given by Lemma 3.3.2. A numerical example is given in Section 3.5 to illustrates the main issues raised in our research. / Thesis (M.Sc. (Applied Mathematics))--North-West University, Potchefstroom Campus, 2011.

[pt] No Brasil, existem muitos projetos em sistemas fotovoltaicos, e a projeção para os próximos anos é de crescimento devido incentivos governamentais e os elevados preços das tarifas de energia. Associado a isso, o mercado de armazenamento de energia com baterias de íons de lítio tem se mostrado promissor devido a uma considerável queda nos preços dessas baterias nos últimos anos. Isso pode representar uma oportunidade para o mercado de sistemas fotovoltaicos quando os incentivos acabarem. Este trabalho propõe um modelo PLIM (Programação Linear Inteira Mista) estocástico para dimensionar um sistema fotovoltaico integrado com armazenamento de energia em baterias para grandes consumidores de energia, usando cenários de geração e consumo, podendo considerar diferentes modalidades tarifárias. As variáveis de decisão são o número de painéis, inversores, baterias, a operação diária do sistema de armazenamento e a demanda contratada do consumidor. A função objetivo busca minimizar o custo de investimento no sistema fotovoltaico, baterias e fatura de energia. A abordagem proposta será analisada sob diferentes premissas, uma com incentivos governamentais sobre a anergia injetada na rede e outra na qual a injeção de energia na rede não é permitida, a fim de avaliar a importância das baterias para manter a atratividade econômica do sistema fotovoltaico. Os resultados indicaram que o efeito sinérgico do sistema fotovoltaico com baterias potencializa a arbitragem, que está relacionada com a diferença entre as tarifas de energia de ponta e fora ponta. Isso ocorre principalmente com operação zero exportação porque somente assim o consumidor é livre para escolher a capacidade do seu sistema fotovoltaico, que hoje é limitado no Brasil quando o sistema é conectado na rede de distribuição. / [en] In Brazil, there are many projects in photovoltaic systems, and the projection for the coming years is positive due to the government incentives and the expensive energy tariffs. Associated with this, the Lithium-ion battery storage systems market has been promising due to a significant drop in battery prices in the last few years. This may represent an opportunity for the photovoltaic system market when the incentives run-out. This work proposes a stochastic mixed integer linear programming (MILP) model to design a photovoltaic system integrated with battery energy storage for big electricity consumers, using generation and consumption scenarios, being able to consider different Time-of-Use tariffs. The decision variables are the number of panels, inverters and batteries, its daily operation and the power demand contracted. The objective function aims to minimize the cost of investment, in the photovoltaic system, batteries and electricity bill. The proposed approach will be analyzed under different assumptions, one with the government incentive about injected surplus and another in which the injection into the network is not possible, in order to assess the importance of a storage system to keep the economic attraction of the photovoltaic system. Results indicated that the synergic effect of the photovoltaic system and battery potentialize the arbitrage, which is related to the difference between peak and off-peak energy tariff. This occurs, mainly with Zero Export operation because only this way the consumer is free to choose the capacity of the photovoltaic system, which is limited in Brazil when the system is allowed to inject energy into the network.

[ES] La gran mayoría de procesos del mundo real tienen incertidumbres inherentes, las cuales, al ser consideradas en el proceso de modelado, se puede obtener una representación que describa con la mayor precisión posible el comportamiento del proceso real. En la mayoría de casos prácticos, se considera que éstas tienen un comportamiento estocástico y sus descripciones como distribuciones de probabilidades son conocidas. Las estrategias de MPC estocástico están desarrolladas para el control de procesos con incertidumbres de naturaleza estocástica, donde el conocimiento de las propiedades estadísticas de las incertidumbres es aprovechado al incluirlo en el planteamiento de un problema de control óptimo (OCP). En éste, y contrario a otros esquemas de MPC, las restricciones duras son relajadas al reformularlas como restricciones de tipo probabilísticas con el fin de reducir el conservadurismo. Esto es, se permiten las violaciones de las restricciones duras originales, pero tales violaciones no deben exceder un nivel de riesgo permitido. La no-convexidad de tales restricciones probabilísticas hacen que el problema de optimización sea prohibitivo, por lo que la mayoría de las estrategias de MPC estocástico en la literatura se diferencian en la forma en que abordan tales restricciones y las incertidumbres, para volver el problema computacionalmente manejable. Por un lado, están las estrategias deterministas que, fuera de línea, convierten las restricciones probabilísticas en unas nuevas de tipo deterministas, usando la propagación de las incertidumbres a lo largo del horizonte de predicción para ajustar las restricciones duras originales. Por otra parte, las estrategias basadas en escenarios usan la información de las incertidumbres para, en cada instante de muestreo, generar de forma aleatoria un conjunto de posibles evoluciones de éstas a lo largo del horizonte de predicción. De esta manera, convierten las restricciones probabilísticas en un conjunto de restricciones deterministas que deben cumplirse para todos los escenarios generados. Estas estrategias se destacan por su capacidad de incluir en tiempo real información actualizada de las incertidumbres. No obstante, esta ventaja genera inconvenientes como su gasto computacional, el cual aumenta conforme lo hace el número de escenarios y; por otra parte, el efecto no deseado en el problema de optimización, causado por los escenarios con baja probabilidad de ocurrencia, cuando se usa un conjunto de escenarios pequeño. Los retos mencionados anteriormente orientaron esta tesis hacia los enfoques de MPC estocástico basado en escenarios, produciendo tres contribuciones principales. La primera consiste en un estudio comparativo de un algoritmo del grupo determinista con otro del grupo basado en escenarios; se hace un especial énfasis en cómo cada uno de estos aborda las incertidumbres, transforma las restricciones probabilísticas y en la estructura de su OCP, además de señalar sus aspectos más destacados y desafíos. La segunda contribución es una nueva propuesta de algoritmo MPC, el cual se basa en escenarios condicionales, diseñado para sistemas lineales con incertidumbres correlacionadas. Este esquema aprovecha la existencia de tal correlación para convertir un conjunto de escenarios inicial de gran tamaño en un conjunto de escenarios más pequeño con sus probabilidades de ocurrencia, el cual conserva las características del conjunto inicial. El conjunto reducido es usado en un OCP en el que las predicciones de los estados y entradas del sistema son penalizadas de acuerdo con las probabilidades de los escenarios que las componen, dando menor importancia a los escenarios con menores probabilidades de ocurrencia. La tercera contribución consiste en un procedimiento para la implementación del nuevo algoritmo MPC como gestor de la energía en una microrred en la que las previsiones de las energías renovables y las cargas están correlacionadas. / [CA] La gran majoria de processos del món real tenen incerteses inherents, les quals, en ser considerades en el procés de modelatge, es pot obtenir una representació que descriga amb la major precisió possible el comportament del procés real. En la majoria de casos pràctics, es considera que aquestes tenen un comportament estocàstic i les seues descripcions com a distribucions de probabilitats són conegudes. Les estratègies de MPC estocàstic estan desenvolupades per al control de processos amb incerteses de naturalesa estocàstica, on el coneixement de les propietats estadístiques de les incerteses és aprofitat en incloure'l en el plantejament d'un problema de control òptim (OCP). En aquest, i contrari a altres esquemes de MPC, les restriccions dures són relaxades en reformulades com a restriccions de tipus probabilístiques amb la finalitat de reduir el conservadorisme. Això és, es permeten les violacions de les restriccions dures originals, però tals violacions no han d'excedir un nivell de risc permès. La no-convexitat de tals restriccions probabilístiques fan que el problema d'optimització siga computacionalment immanejable, per la qual cosa la majoria de les estratègies de MPC estocàstic en la literatura es diferencien en la forma en què aborden tals restriccions i les incerteses, per a tornar el problema computacionalment manejable. D'una banda, estan les estratègies deterministes que, fora de línia, converteixen les restriccions probabilístiques en unes noves de tipus deterministes, usant la propagació de les incerteses al llarg de l'horitzó de predicció per a ajustar les restriccions dures originals. D'altra banda, les estratègies basades en escenaris usen la informació de les incerteses per a, en cada instant de mostreig, generar de manera aleatòria un conjunt de possibles evolucions d'aquestes al llarg de l'horitzó de predicció. D'aquesta manera, converteixen les restriccions probabilístiques en un conjunt de restriccions deterministes que s'han de complir per a tots els escenaris generats. Aquestes estratègies es destaquen per la seua capacitat d'incloure en temps real informació actualitzada de les incerteses. No obstant això, aquest avantatge genera inconvenients com la seua despesa computacional, el qual augmenta conforme ho fa el nombre d'escenaris i; d'altra banda, l'efecte no desitjat en el problema d'optimització, causat pels escenaris amb baixa probabilitat d'ocurrència, quan s'usa un conjunt d'escenaris xicotet. Els reptes esmentats anteriorment van orientar aquesta tesi cap als enfocaments de MPC estocàstic basat en escenaris, produint tres contribucions principals. La primera consisteix en un estudi comparatiu d'un algorisme del grup determinista amb un altre del grup basat en escenaris; on es fa un especial èmfasi en com cadascun d'aquests aborda les incerteses, transforma les restriccions probabilístiques i en l'estructura del seu problema d'optimització, a més d'assenyalar els seus aspectes més destacats i desafiaments. La segona contribució és una nova proposta d'algorisme MPC, el qual es basa en escenaris condicionals, dissenyat per a sistemes lineals amb incerteses correlacionades. Aquest esquema aprofita l'existència de tal correlació per a convertir un conjunt d'escenaris inicial de gran grandària en un conjunt d'escenaris més xicotet amb les seues probabilitats d'ocurrència, el qual conserva les característiques del conjunt inicial. El conjunt reduït és usat en un OCP en el qual les prediccions dels estats i entrades del sistema són penalitzades d'acord amb les probabilitats dels escenaris que les componen, donant menor importància als escenaris amb menors probabilitats d'ocurrència. La tercera contribució consisteix en un procediment per a la implementació del nou algorisme MPC com a gestor de l'energia en una microxarxa en la qual les previsions de les energies renovables i les càrregues estan correlacionades. / [EN] The vast majority of real-world processes have inherent uncertainties, which, when considered in the modelling process, can provide a representation that most accurately describes the behaviour of the real process. In most practical cases, these are considered to have stochastic behaviour and their descriptions as probability distributions are known. Stochastic model predictive control algorithms are developed to control processes with uncertainties of a stochastic nature, where the knowledge of the statistical properties of the uncertainties is exploited by including it in the optimal control problem (OCP) statement. Contrary to other model predictive control (MPC) schemes, hard constraints are relaxed by reformulating them as probabilistic constraints to reduce conservatism. That is, violations of the original hard constraints are allowed, but such violations must not exceed a permitted level of risk. The non-convexity of such probabilistic constraints renders the optimisation problem computationally unmanageable, thus most stochastic MPC strategies in the literature differ in how they deal with such constraints and uncertainties to turn the problem computationally tractable. On the one hand, there are deterministic strategies that, offline, convert probabilistic constraints into new deterministic ones, using the propagation of uncertainties along the prediction horizon to tighten the original hard constraints. Scenario-based approaches, on the other hand, use the uncertainty information to randomly generate, at each sampling instant, a set of possible evolutions of uncertainties over the prediction horizon. In this fashion, they convert the probabilistic constraints into a set of deterministic constraints that must be fulfilled for all the scenarios generated. These strategies stand out for their ability to include real-time updated uncertainty information. However, this advantage comes with inconveniences such as computational effort, which grows as the number of scenarios does, and the undesired effect on the optimisation problem caused by scenarios with a low probability of occurrence when a small set of scenarios is used. The aforementioned challenges steered this thesis toward stochastic scenario-based MPC approaches, and yielded three main contributions. The first one consists of a comparative study of an algorithm from the deterministic group with another one from the scenario-based group, where a special emphasis is made on how each of them deals with uncertainties, transforms the probabilistic constraints and on the structure of the optimisation problem, as well as pointing out their most outstanding aspects and challenges. The second contribution is a new proposal for a MPC algorithm, which is based on conditional scenarios, developed for linear systems with correlated uncertainties. This scheme exploits the existence of such correlation to convert a large initial set of scenarios into a smaller one with their probabilities of occurrence, which preserves the characteristics of the initial set. The reduced set is used in an OCP in which the predictions of the system states and inputs are penalised according to the probabilities of the scenarios that compose them, giving less importance to the scenarios with lower probabilities of occurrence. The third contribution consists of a procedure for the implementation of the new MPC algorithm as an energy manager in a microgrid in which the forecasts of renewables and loads are correlated. / González Querubín, EA. (2024). Scenario-Based Model Predictive Control for Systems with Correlated Uncertainties [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/203887