Wasserwirtschaftliche Speichersysteme sind unverzichtbar, um weltweit die Trinkwasserversorgung, Nahrungsmittelproduktion und Energieversorgung sicherzustellen. Die multikriterielle simulationsbasierte Optimierung (MK-SBO) ist eine leistungsfähige Methodik, um für Mehrzweck-Talsperrenverbundsysteme (MZ-TVS) eine Pareto-optimale Menge an Kompromisslösungen zwischen konträren Zielen bereitzustellen. Der rechentechnische Aufwand steigt jedoch linear mit der Länge des Simulationszeitraums der Talsperrenbewirtschaftung an. Folglich begrenzen sich MK-SBO-Studien bisher auf Simulationszeiträume von wenigen Jahrzehnten. Diese Zeiträume sind i.d.R. unzureichend, um Unsicherheiten, die aus der stochastischen Natur der Zuflüsse resultieren, adäquat zu beschreiben. Bewirtschaftungsoptimierungen von MZ-TVS hinsichtlich ihrer Zuverlässigkeit, z.B. durch die Maximierung von Versorgungssicherheiten, können sich als wenig belastbar und ermittelte Steuerungsstrategien als wenig robust erweisen. Um diesen Herausforderungen zu begegnen, wird ein neues modulares Framework zur multikriteriellen simulationsbasierten Bewirtschaftungsoptimierung von MZ-TVS (Frams-BoT) entwickelt. Eine Informationserweiterung zu stochastischen Zuflussprozessen erfolgt über ein weiterentwickeltes Zeitreihenmodell mittels generierter Zeitreihen von mehreren Tausend Jahren Länge. Eine neue Methode zur Monte-Carlo-Rekombination von Zeitreihen ermöglicht dann die Nutzung dieser Informationen in der MK-SBO in wesentlich kürzeren Simulationszeiträumen. Weitere Rechenzeit wird durch Parallelisierung und eine fortgeschrittene Kodierung von Entscheidungsvariablen eingespart. Die Simulation von Zuflussdargeboten für multikriterielle Klimafolgenanalysen erfolgt durch ein prozessorientiertes Wasserhaushaltsmodell. Level-Diagramme (Blasco et al., 2008) unterstützten den komplexen Prozess der Entscheidungsfindung. Die Wirksamkeit und Flexibilität des Frameworks wurden in zwei Fallstudien gezeigt. In einer ersten Fallstudie konnten in einer Klimafolgenanalyse Versorgungssicherheiten von über 99% als ein Ziel eines multikriteriellen Optimierungsproblems maximiert werden, um die Verlässlichkeit der Bewirtschaftung eines MZ-TVS in Sachsen (Deutschland) zu steigern. Eine zweite Fallstudie befasste sich mit der Maximierung der Leistungsfähigkeit eines MZ-TVS in Äthiopien unter verschiedenen Problemformulierungen. In beiden Fallstudien erwiesen sich die erzielten Pareto-Fronten und Steuerungsstrategien gegenüber 10 000-jährigen Zeiträumen als robust. Die benötigten Rechenzeiten der MK-SBO ließen sich durch das Framework massiv senken. / Water resources systems are worldwide essential for a secure supply of potable water, food and energy production. Simulation-based multi-objective optimization (SB-MOO) is a powerful method to provide a set of Pareto-optimal compromise solutions between various contrary goals of multi-purpose multi-reservoir systems (MP-MRS). However, the computational costs increases with the length of the time period in which the reservoir management is simulated. Consequently, MK-SBO studies are currently restricted to simulation periods of several decades. These time periods are normally insufficient to describe the stochastic nature of the inflows and the consequent hydrological uncertainties. Therefore, an optimization of the reliability of management of MP-MRS, e.g. through the maximization of the security of supply, may not be resilient. Obtained management strategies may not prove robust. To address these challenges, a new modular framework for simulation-based multiobjective optimization of the reservoir management of multi-purpose multi-reservoir systems (Frams-BoT) is developed. A refined time series model provides time series of several thousand years to extend the available information about the stochastic inflow processes. Then, a new Monte-Carlo recombination method allows for the exploitation of the extended information in the SB-MOO on significantly shorter time periods. Further computational time is saved by parallelization and an advanced coding of decision variables. A processoriented water balance model is used to simulate inflows for multi-objective climate impact analysis. Level-Diagrams [Blasco et al., 2008] are used to support the complex process of decision-making. The effectiveness and flexibility of the framework is presented in two case studies. In the first case study about a MP-MRS in Germany, high securities of supply over 99% where maximized as part of a multi-objective optimization problem in order to improve the reliability of the reservoir management. A second case study addressed the maximization of the performance of a MP-MRS in Ethiopia under different formulations of the optimization problem. In both case studies, the obtained Pareto-Fronts and management strategies proved robust compared to 10 000 year time periods. The required computational times of the SB-MOO could be reduced considerably.

To meet the exponentially increasing traffic data driven by the rapidly growing mobile subscriptions, both industry and academia are exploring the potential of a new genera- tion (5G) of wireless technologies. An important 5G goal is to achieve high data rate. Small cells with spectrum sharing and multiple-input multiple-output (MIMO) techniques are one of the most promising 5G technologies, since it enables to increase the aggregate data rate by improving the spectral efficiency, nodes density and transmission bandwidth, respectively. However, the increased interference in the densified networks will in return limit the achievable rate performance if not properly managed. The considered setup can be modeled as MIMO interference networks, which can be classified into the K-user MIMO interference channel (IC) and the K-cell MIMO interfering broadcast channel/multiple access channel (MIMO-IBC/IMAC) according to the number of mobile stations (MSs) simultaneously served by each base station (BS). The thesis considers two physical layer (PHY) resource allocation problems that deal with the interference for both models: 1) Pareto boundary computation for the achiev- able rate region in a K-user single-stream MIMO IC and 2) grouping-based interference alignment (GIA) with optimized IA-Cell assignment in a MIMO-IMAC under limited feedback. In each problem, the thesis seeks to provide a deeper understanding of the system and novel mathematical results, along with supporting numerical examples. Some of the main contributions can be summarized as follows. It is an open problem to compute the Pareto boundary of the achievable rate region for a K-user single-stream MIMO IC. The K-user single-stream MIMO IC models multiple transmitter-receiver pairs which operate over the same spectrum simultaneously. Each transmitter and each receiver is equipped with multiple antennas, and a single desired data stream is communicated in each transmitter-receiver link. The individual achievable rates of the K users form a K-dimensional achievable rate region. To find efficient operating points in the achievable rate region, the Pareto boundary computation problem, which can be formulated as a multi-objective optimization problem, needs to be solved. The thesis transforms the multi-objective optimization problem to two single-objective optimization problems–single constraint rate maximization problem and alternating rate profile optimization problem, based on the formulations of the ε-constraint optimization and the weighted Chebyshev optimization, respectively. The thesis proposes two alternating optimization algorithms to solve both single-objective optimization problems. The convergence of both algorithms is guaranteed. Also, a heuristic initialization scheme is provided for each algorithm to achieve a high-quality solution. By varying the weights in each single-objective optimization problem, numerical results show that both algorithms provide an inner bound very close to the Pareto boundary. Furthermore, the thesis also computes some key points exactly on the Pareto boundary in closed-form. A framework for interference alignment (IA) under limited feedback is proposed for a MIMO-IMAC. The MIMO-IMAC well matches the uplink scenario in cellular system, where multiple cells share their spectrum and operate simultaneously. In each cell, a BS receives the desired signals from multiple MSs within its own cell and each BS and each MS is equipped with multi-antenna. By allowing the inter-cell coordination, the thesis develops a distributed IA framework under limited feedback from three aspects: the GIA, the IA-Cell assignment and dynamic feedback bit allocation (DBA), respec- tively. Firstly, the thesis provides a complete study along with some new improvements of the GIA, which enables to compute the exact IA precoders in closed-form, based on local channel state information at the receiver (CSIR). Secondly, the concept of IA-Cell assignment is introduced and its effect on the achievable rate and degrees of freedom (DoF) performance is analyzed. Two distributed matching approaches and one centralized assignment approach are proposed to find a good IA-Cell assignment in three scenrios with different backhaul overhead. Thirdly, under limited feedback, the thesis derives an upper bound of the residual interference to noise ratio (RINR), formulates and solves a corresponding DBA problem. Finally, numerical results show that the proposed GIA with optimized IA-Cell assignment and the DBA greatly outperforms the traditional GIA algorithm.

De nos jours, de nombreux systèmes de production sont gérés en flux « tirés » et utilisent des méthodes basées sur des « cartes », comme : Kanban, ConWIP, COBACABANA, etc. Malgré leur simplicité et leur efficacité, ces méthodes ne sont pas adaptées lorsque la production n’est pas stable et que la demande du client varie. Dans de tels cas, les systèmes de production doivent donc adapter la tension de leur flux tout au long du processus de fabrication. Pour ce faire, il faut déterminer comment ajuster dynamiquement le nombre de cartes (ou de ‘e-card’) en fonction du contexte. Malheureusement, ces décisions sont complexes et difficiles à prendre en temps réel. De plus, dans certains cas, changer trop souvent le nombre de cartes kanban peut perturber la production et engendrer un problème de nervosité. Les opportunités offertes par l’industrie 4.0 peuvent être exploitées pour définir des stratégies intelligentes de pilotage de flux permettant d’adapter dynamiquement ce nombre de cartes kanban.Dans cette thèse, nous proposons, dans un premier temps, une approche adaptative basée sur la simulation et l'optimisation multi-objectif, capable de prendre en considération le problème de la nervosité et de décider de manière autonome (ou d'aider les gestionnaires)  quand et où ajouter ou retirer des cartes Kanban. Dans un deuxième temps, nous proposons une nouvelle approche adaptative et intelligente basée sur un réseau de neurones dont l’apprentissage est d’abord réalisé hors ligne à l’aide d’un modèle numérique jumeau (simulation), exploité par une optimisation multi-objectif. Après l’apprentissage, le réseau de neurones permet de décider en temps réel, quand et à quelle étape de fabrication il est pertinent de changer le nombre de cartes kanban. Des comparaisons faites avec les meilleures méthodes publiées dans la littérature montrent de meilleurs résultats avec des changements moins fréquents. / Today, many production systems are managed in "pull" control system and used "card-based" methods such as: Kanban, ConWIP, COBACABANA, etc. Despite their simplicity and efficiency, these methods are not suitable when production is not stable and customer demand varies. In such cases, the production systems must therefore adapt the “tightness” of their production flow throughout the manufacturing process. To do this, we must determine how to dynamically adjust the number of cards (or e-card) depending on the context. Unfortunately, these decisions are complex and difficult to make in real time. In addition, in some cases, changing too often the number of kanban cards can disrupt production and cause a nervousness problem. The opportunities offered by Industry 4.0 can be exploited to define smart flow control strategies to dynamically adapt this number of kanban cards.In this thesis, we propose, firstly, an adaptive approach based on simulation and multi-objective optimization technique, able to take into account the problem of nervousness and to decide autonomously (or to help managers) when and where adding or removing Kanban cards. Then, we propose a new adaptive and intelligent approach based on a neural network whose learning is first realized offline using a twin digital model (simulation) and exploited by a multi-objective optimization method. Then, the neural network could be able to decide in real time, when and at which manufacturing stage it is relevant to change the number of kanban cards. Comparisons made with the best methods published in the literature show better results with less frequent changes.

<p>As society works to reduce its reliance on fossil fuels, the demand for renewable energy and clean energy technologies continues to grow rapidly. Lessons learned from the ongoing electronics waste crisis necessitate closing material loops to secure supply chains and redirect valuable resources away from the landfills.</p><p> </p><p>Inspired by the principles of industrial ecology, the circularization of renewables is demonstrated by applying the notion of Life Cycle Symbiosis (LCS), an extension of Industrial Symbiosis (IS). This is achieved by identifying waste streams that may have value as potential raw material/feedstock and the subsequent development of industrial synergies in the context of end of life (EoL) photovoltaics (PVs). Per metric ton of EoL PVs, the avoided global warming potential (GWP) and ecotoxicity impacts were calculated to be as high as 2750 kg CO₂ eq and 32,000 CTUe respectively, while the water savings and electricity savings were over 37,000 m³ and 3600 MJ. Building upon this work, a hybrid multi objective optimization (MOO) method was proposed to support the creation of industrial synergistic networks or eco industrial parks (EIPs). The hybrid method addresses the challenges associated with the early design and development stages of EIPs (supply, demand, potential synergies, etc.), and also those in relation to considering multiple conflicting sustainability objectives.</p><p> </p><p>Apart from addressing material scarcity, rising pollution levels and exposure to toxins, recovery and circularization may also contribute towards stabilizing feedstock prices. Supply chains for renewables and clean energy technologies are brittle because of risks associated with possible supply deficits stemming from complex geo-political situations and oligopolies. This can translate to price fluctuations among high-value, critical materials on which clean energy technologies rely. In order to ensure a smooth transition to a clean energy technologies, and one that is also sustainable, it is vital to assess the impact of these very complexities on the market dynamics for the critical material feedstocks. To this end, a system dynamics model has been developed to capture price trends of rare earth elements (REEs) used in EVs under varying market scenarios. The proposed model aims to aid automobile manufacturers in developing effective business strategies as they work towards electrifying their vehicle fleets.</p><p>This thesis reports on the development of some strategies rooted in industrial ecology to prevent renewables and clean energy technologies from themselves becoming environmental liabilities in the future.</p>

No description available.

In the building automation domain, there are gaps among various tasks related to design engineering. As a result created system designs must be adapted to the given requirements on system functionality, which is related to increased costs and engineering effort than planned. For this reason standards are prepared to enable a coordination among these tasks by providing guidelines and unified artifacts for the design. Moreover, a huge variety of prefabricated devices offered from different manufacturers on the market for building automation that realize building automation functions by preprogrammed software components. Current methods for design creation do not consider this variety and design solution is limited to product lines of a few manufacturers and expertise of system integrators. Correspondingly, this results in design solutions of a limited quality. Thus, a great optimization potential of the quality of design solutions and coordination of tasks related to design engineering arises. For given design requirements, the existence of a high number of devices that realize required functions leads to a combinatorial explosion of design alternatives at different price and quality levels. Finding optimal design alternatives is a hard problem to which a new solution method is proposed based on heuristical approaches. By integrating problem specific knowledge into algorithms based on heuristics, a promisingly high optimization performance is achieved. Further, optimization algorithms are conceived to consider a set of flexibly defined quality criteria specified by users and achieve system design solutions of high quality. In order to realize this idea, optimization algorithms are proposed in this thesis based on goal-oriented operations that achieve a balanced convergence and exploration behavior for a search in the design space applied in different strategies. Further, a component model is proposed that enables a seamless integration of design engineering tasks according to the related standards and application of optimization algorithms.:1 Introduction 17 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.3 Goals and Use of the Thesis . . . . . . . . . . . . . . . . . . . . . 21 1.4 Solution Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.5 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . 24 2 Design Creation for Building Automation Systems 25 2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.2 Engineering of Building Automation Systems . . . . . . . . . . . 29 2.3 Network Protocols of Building Automation Systems . . . . . . . 33 2.4 Existing Solutions for Design Creation . . . . . . . . . . . . . . . 34 2.5 The Device Interoperability Problem . . . . . . . . . . . . . . . . 37 2.6 Guidelines for Planning of Room Automation Systems . . . . . . 38 2.7 Quality Requirements on BAS . . . . . . . . . . . . . . . . . . . 41 2.8 Quality Requirements on Design . . . . . . . . . . . . . . . . . . 42 2.8.1 Quality Requirements Related to Project Planning . . . . 42 2.8.2 Quality Requirements Related to Project Implementation 43 2.9 Quality Requirements on Methods . . . . . . . . . . . . . . . . . 44 2.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3 The Design Creation Task 47 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2 System Design Composition Model . . . . . . . . . . . . . . . . . 49 3.2.1 Abstract and Detailed Design Model . . . . . . . . . . . . 49 3.2.2 Mapping Model . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3 Formulation of the Problem . . . . . . . . . . . . . . . . . . . . . 53 3.3.1 Problem properties . . . . . . . . . . . . . . . . . . . . . . 54 3.3.2 Requirements on Algorithms . . . . . . . . . . . . . . . . 56 3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4 Solution Methods for Design Generation and Optimization 59 4.1 Combinatorial Optimization . . . . . . . . . . . . . . . . . . . . . 59 4.2 Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.3 Examples for Metaheuristics . . . . . . . . . . . . . . . . . . . . . 62 4.3.1 Simulated Annealing . . . . . . . . . . . . . . . . . . . . . 62 4.3.2 Tabu Search . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3.3 Ant Colony Optimization . . . . . . . . . . . . . . . . . . 65 4.3.4 Evolutionary Computation . . . . . . . . . . . . . . . . . 66 4.4 Choice of the Solver Algorithm . . . . . . . . . . . . . . . . . . . 69 4.5 Specialized Methods for Diversity Preservation . . . . . . . . . . 70 4.6 Approaches for Real World Problems . . . . . . . . . . . . . . . . 71 4.6.1 Component-Based Mapping Problems . . . . . . . . . . . 71 4.6.2 Network Design Problems . . . . . . . . . . . . . . . . . . 73 4.6.3 Comparison of Solution Methods . . . . . . . . . . . . . . 74 4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5 Automated Creation of Optimized Designs 79 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.2 Design Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.3 Component Model . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.3.1 Presumptions . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.3.2 Integration of Component Model . . . . . . . . . . . . . . 87 5.4 Design Generation . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.4.1 Component Search . . . . . . . . . . . . . . . . . . . . . . 88 5.4.2 Generation Approaches . . . . . . . . . . . . . . . . . . . 100 5.5 Design Improvement . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.5.1 Problems and Requirements . . . . . . . . . . . . . . . . . 107 5.5.2 Variations . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 5.5.3 Application Strategies . . . . . . . . . . . . . . . . . . . . 121 5.6 Realization of the Approach . . . . . . . . . . . . . . . . . . . . . 122 5.6.1 Objective Functions . . . . . . . . . . . . . . . . . . . . . 122 5.6.2 Individual Representation . . . . . . . . . . . . . . . . . . 123 5.7 Automated Design Creation For A Building . . . . . . . . . . . . 124 5.7.1 Room Spanning Control . . . . . . . . . . . . . . . . . . . 124 5.7.2 Flexible Rooms . . . . . . . . . . . . . . . . . . . . . . . . 125 5.7.3 Technology Spanning Designs . . . . . . . . . . . . . . . . 129 5.7.4 Preferences for Mapping of Function Blocks to Devices . . 132 5.8 Further Uses and Applicability of the Approach . . . . . . . . . . 133 5.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 6 Validation and Performance Analysis 137 6.1 Validation Method . . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.2 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.3 Example Abstract Designs and Performance Tests . . . . . . . . 139 6.3.1 Criteria for Choosing Example Abstract Designs . . . . . 139 6.3.2 Example Abstract Designs . . . . . . . . . . . . . . . . . . 140 6.3.3 Performance Tests . . . . . . . . . . . . . . . . . . . . . . 142 6.3.4 Population Size P - Analysis . . . . . . . . . . . . . . . . 151 6.3.5 Cross-Over Probability pC - Analysis . . . . . . . . . . . 157 6.3.6 Mutation Probability pM - Analysis . . . . . . . . . . . . 162 6.3.7 Discussion for Optimization Results and Example Designs 168 6.3.8 Resource Consumption . . . . . . . . . . . . . . . . . . . . 171 6.3.9 Parallelism . . . . . . . . . . . . . . . . . . . . . . . . . . 172 6.4 Optimization Framework . . . . . . . . . . . . . . . . . . . . . . . 172 6.5 Framework Design . . . . . . . . . . . . . . . . . . . . . . . . . . 174 6.5.1 Components and Interfaces . . . . . . . . . . . . . . . . . 174 6.5.2 Workflow Model . . . . . . . . . . . . . . . . . . . . . . . 177 6.5.3 Optimization Control By Graphical User Interface . . . . 180 6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 7 Conclusions 185 A Appendix of Designs 189 Bibliography 201 Index 211

<p dir="ltr">Evidence for climate change-related impacts and risks is already widespread globally, affecting not only the ecosystems but also the economy and health of our communities. Data-driven predictive modeling approaches such as machine learning and deep learning have emerged to be powerful tools for interpreting large and complex non-linear datasets such as meteorological variables from weather stations or the distribution of infectious droplets produced in a cough. However, the strength of these data-driven models can be further optimized by complementing them with foundational knowledge of the physical processes they represent. By understanding the core physics, one can enhance the reliability and accuracy of predictive outcomes. The effectiveness of these combined approaches becomes particularly feasible and robust with the recent advancements in the High-Performance Computing field. With improved processing speed, algorithm design, and storage capabilities, modern computers allow for a deeper and more precise examination of the data. Such advancements equip us to address the diverse challenges presented by climate change more effectively.</p><p dir="ltr">In particular, this document advances research in mitigating and preventing the consequences of global warming by implementing data-driven predictive models based on statistical, machine learning, and deep learning methods via two phases. In the first phase, this dissertation proposes frameworks consisting of machine and deep learning algorithms to increase the resilience of small-scale renewable energy systems, which are essential for reducing greenhouse gas emissions in the ecosystems. The second phase focuses on using data from physics-based models, i.e., computational fluid dynamics (CFD), in data-driven predictive models for improving the design of air cleaning technologies, which are crucial to reducing the transmission of infectious diseases in indoor environments. </p><p dir="ltr">Specifically, this work is an article-based collection of published (or will be published) research articles. The articles are reformatted to fit the thesis's structure. The contents of the original articles are self-contained. </p>

Hochtemperatur-Konversionsprozesse sind ein wesentlicher Bestandteil von industriellen Produktionsprozessen, die maßgeblich den Prozesswirkungsgrad und die Produktionskosten beeinflussen. Die modellgestützte Optimierung ermöglicht eine gezielte Verbesserung verschiedener Parameter unter Berücksichtigung von prozesstechnischen, ökonomischen und ökologischen Aspekten. Bisher existiert in der Literatur kein Vergleich der Einsatzmöglichkeiten verschiedener Modellierungsmethoden zur modellgestützten, multikriteriellen Optimierung von Hochtemperatur-Konversionsprozessen. Daher werden in dieser Arbeit drei exemplarische Konversionsprozesse mit unterschiedlichen Modellierungsmethoden optimiert und anhand der Ergebnisse die Potenziale und Einsatzgrenzen für die modellgestützte Optimierung bewertet. Die Modellierung eines Wirbelschichtvergasers zeigt, dass detaillierte CFD-Modelle für komplexe mehrphasige Prozesse zu rechenaufwändig sind. Hingegen ist für einfache einphasige Prozesse wie ein Quench-Reaktor die Optimierung mit reduzierten CFD-Modellen realisierbar. Die Integration von Ersatzmodellen beschleunigt das Optimierungskonzept bei gleicher Ergebnisqualität, was die Optimierung von komplexen Prozessen für einfache Optimierungsprobleme ermöglicht. Die Optimierung der Partialoxidation von flüssigen Einsatzstoffen zur Methanolproduktion zeigt, dass sich Fließbildmodelle gut zur Optimierung von vollständigen Produktionsprozessen und komplexen Optimierungsproblemen eignen. Die Ergebnisse dieser Arbeit können als Basis für die Erstellung von Modellierungs- und Optimierungskonzepten für weitere Hochtemperatur-Konversionsprozesse genutzt werden.

Zukünftige Beschleunigerprojekte und Nutzerexperimente erfordern für ihren Betrieb einen hochbrillanten Elektronenstrahl mit hohem mittlerem Strom. Eine Elektronenquelle mit dem Potential die Anforderungen erfüllen, ist ein supraleitender Hochfrequenz (SHF) Photoinjektor im Dauerstrichbetrieb. Die Strahldynamik eines solchen Photoinjektor Systems bestimmt die maximal zu erreichende Strahlbrillanz und wird ihrerseits von den Design und Betriebsparametern des Photoinjektors beeinflusst. Ziel ist immer die entscheidenden Design- und Betriebsparameter der Elektronenquelle hinsichtlich einer maximalen Strahlbrillanz zu wählen. Diese Aufgabe verlangt ein detailliertes Verständnis der Strahldynamik-Prozesse. Ferner ist es notwendig, eine Optimierung des Photoinjektors als Ganzes, mit dem Ziel einer maximalen Strahlqualität bei hohem mittlerem Strom, vorzunehmen. Dieses ermöglicht auch, die physikalischen Grenzen eines gegebenen Designs zu ermitteln und im Betrieb vollständig auszunutzen. Diese Doktorarbeit befasst sich mit der Strahldynamik in einem SHF Photoinjektor, unter Berücksichtigung interner Raumladungseffekte. Die Erkenntnisse zur Strahldynamik werden für die Entwicklung eines Optimierungsprogramms verwendet, um die Leistung des Injektors hinsichtlich der Strahlbrillanz zu verbessern. Die entwickelte Methode basiert auf Pareto-Optimierung mehrerer Zielfunktionen, unter Verwendung eines generischen Algorithmus. Das zentrale Ergebnis dieser Arbeit umfasst ein universelles Optimierungsprogramm, das für Photoinjektoren unabhängig von ihrem Design und Anwendungsgebiet genutzt werden kann. Für den Betrieb mit hoher Strahlbrillanz ist es möglich aus den erhaltenen Pareto-optimalen Lösungen einen stabilen Satz an Einstellwerten für den Photoinjektor zu extrahieren. Durch die allgemeine Optimierungsstrategie lässt sich das entwickelte Programm auch für andere Beschleunigerabschnitte, oder die Optimierung einer ganzen Anlage mit erweiterter Zielsetzung anpassen. / An increasing number of future accelerator projects, light sources and user experiments require high brightness, high average current electron beams for operation. Superconducting radio-frequency (SRF) photoinjectors running in continuous-wave (cw) mode hold the potential to serve as an electron source that generates electron beams of high brightness. Different operation and design parameters of the SRF photoinjector impact the beam dynamics and, thus, the beam brightness. Therefore, an in-depth understanding of the beam dynamics processes in an SRF photoinjector and the dependency of the beam dynamics on the photoinjector set parameters is crucial. A high brightness beam operation requires a global optimization of the SRF photoinjector that allows to find suitable photoinjector settings and to figure out and extend the physical performance limits of the investigated injector design. The dissertation at hand offers a detailed analysis of the beam dynamics in an SRF photoinjector regarding internal space charge effects. Furthermore, the impact of the photoinjector elements on the electron beam is discussed. The lessons learned from this theoretical view are implemented in the development of an optimization tool to achieve a high brightness performance. A universal multi-objective optimization program based on a generic algorithm was developed to extract stable, optimum gun parameter from Pareto-optimum solutions. This universal tool is able to optimize and find the physical performance limit of any (S)RF photoinjector independent from the individual application of the electron source (energy recovery linac, free electron laser, ultra-fast electron diffraction). This thesis thereby verifies and complements existing theoretical considerations regarding photoinjector-beam interactions. The global optimization strategy can be introduced to variable optimization objectives as well as it can be extended to an optimization of further parts of the accelerator facility.

This report summarizes the thesis study carried out at Scania CV AB. The study presents design optimization of an interior permanent magnetic (IPM) synchronous machine. A parameterised Matlab tool was developed in order to serve the optimization routine. In this routine, multiple IPM geometries are dynamically generated and analyzed using the finite element method (FEM) software Flux 2D. Using the Secant method, algorithms to estimate the optimum current and control angle throughout the speed range both below and above base speed, were developed and integrated with the Matlab tool to perform design optimization towards the drive cycle. The genetic algorithm available in the Matlab’s global optimization toolbox has been utilised for the multi-objective optimization setup. / Denna rapport sammanfattar examensarbetet som genomförts på Scania CV AB. Studien presenterar en designoptimering av en permanentmagnetiserad (IPM) synkronmaskin. Ett parametriserat Matlab-verktyg för optimeringar utvecklades for att dynamiskt generera geometrier för att utföra simuleringar med hjälp av FEM-programvara (Flux2D®). Med hjälp av Sekant-metoden utvecklades och integrerades algoritmer för att estimera den optimala strömmen och styrvinkeln i hela varvtalssområdet, både under basvarvtal och i fältförsvagningsområdet. Med hjälp av Matlab-verktyget kunde sedan designoptimering för en given k¨orcykel utföras. Den genetiska algoritmen som finns tillgänglig i Matlabs globala optimeringsverktygslåda har använts för det multi-objektiva optimeringsprogrammet.