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Reliable and regional differentiated power forecasts are required to guarantee an efficient and economic energy transition towards renewable energies. Amongst other renewable energy technologies, e.g. wind mills, photovoltaic (PV) systems are an essential component of this transition being cost-efficient and simply to install. Reliable power forecasts are however required for a grid integration of photovoltaic systems, which among other data requires high-resolution spatio-temporal global irradiance data.
Today, more than 70 million tons of lignin are produced by the pulp and paper industry every year. However, the utilization of lignin as a source for chemical synthesis is still limited due to the complex and heterogeneous lignin structure. The purpose of this study was a selective photodegradation of industrially available kraft lignin in order to obtain appropriate fragments and building block chemicals for further utilization, e.g. polymerization. Thus, kraft lignin obtained from soft wood black liquor by acidification was dissolved in sodium hydroxide and irradiated at a wavelength of 254 nm with and without the presence of titanium dioxide in various concentrations. Analyses of the irradiated products via SEC showed decreasing molar masses and decreasing polydispersity indices over time. At the end of the irradiation period the lignin was depolymerised to form fragments as small as the lignin monomers. TOC analyses showed minimal mineralisation due to the depolymerisation process.
Synthesis of serving policies for objects flow in the system with refillable storage component
(2017)
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique has the potential to be a powerful tool for design space exploration, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination algorithm (SAIL), introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites.
The ability of SAIL to efficiently produce both accurate models and diverse high performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a different combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.
A new method for design space exploration and optimization, Surrogate-Assisted Illumination (SAIL), is presented. Inspired by robotics techniques designed to produce diverse repertoires of behaviors for use in damage recovery, SAIL produces diverse designs that vary according to features specified by the designer. By producing high-performing designs with varied combinations of user-defined features a map of the design space is created. This map illuminates the relationship between the chosen features and performance, and can aid designers in identifying promising design concepts. SAIL is designed for use with compu-tationally expensive design problems, such as fluid or structural dynamics, and integrates approximative models and intelligent sampling of the objective function to minimize the number of function evaluations required. On a 2D airfoil optimization problem SAIL is shown to produce hundreds of diverse designs which perform competitively with those found by state-of-the-art black box optimization. Its capabilities are further illustrated in a more expensive 3D aerodynamic optimization task.
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique to 'illuminate' the problem space through the lens of chosen features has the potential to be a powerful tool for exploring design spaces, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination (SAIL) algorithm, introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites.
The ability of SAIL to efficiently produce both accurate models and diverse high-performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a different combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.
Ressourceneffiziente Optimierung von Hohlkörpern aus Kunststoff mittels Multiskalensimulation
(2017)
Ressourceneffiziente Optimierung von Hohlkörpern aus Kunststoff mittels Multiskalensimulation
(2017)
Die mechanischen Eigenschaften von extrusionsblasgeformten Kunststoffhohlkörpern hängen wesentlich von den vom Verarbeitungsprozess beeinflussten Materialeigenschaften ab. Ziel der dargestellten Untersuchung ist, prozessabhängige Materialkennwerte in Simulationsprogrammen zu berücksichtigen und damit deren Vorhersagegenauigkeit zu erhöhen. Hierzu ist die Schaffung einer Schnittstelle zwischen Prozess- und Bauteilsimulation notwendig. Darüber hinaus wird vorgestellt, wie Simulationen auf Mikroebene (molekulardynamische Simulationen) genutzt werden können, um Materialkennwerte ohne die Durchführung eines Realexperiments zu ermitteln.
Evolutionary illumination is a recent technique that allows producing many diverse, optimal solutions in a map of manually defined features. To support the large amount of objective function evaluations, surrogate model assistance was recently introduced. Illumination models need to represent many more, diverse optimal regions than classical surrogate models. In this PhD thesis, we propose to decompose the sample set, decreasing model complexity, by hierarchically segmenting the training set according to their coordinates in feature space. An ensemble of diverse models can then be trained to serve as a surrogate to illumination.
Neuroevolution methods evolve the weights of a neural network, and in some cases the topology, but little work has been done to analyze the effect of evolving the activation functions of individual nodes on network size, an important factor when training networks with a small number of samples. In this work we extend the neuroevolution algorithm NEAT to evolve the activation function of neurons in addition to the topology and weights of the network. The size and performance of networks produced using NEAT with uniform activation in all nodes, or homogenous networks, is compared to networks which contain a mixture of activation functions, or heterogenous networks. For a number of regression and classification benchmarks it is shown that, (1) qualitatively different activation functions lead to different results in homogeneous networks, (2) the heterogeneous version of NEAT is able to select well performing activation functions, (3) the produced heterogeneous networks are significantly smaller than homogeneous networks.
Die allgemeine Konnotation von Technik mit Männlichkeit hat Auswirkungen auf die Berufswahlentscheidungen und das Technikverständnis von jungen Frauen. Nur gut 22 Prozent aller Studierenden in den Ingenieurswissenschaften waren 2014 in Deutschland weiblich (vgl. MonitorING)1. Seit Jahren wird versucht, diese Zahlen nach oben zu korrigieren, indem man Programme für Mädchen und junge Frauen anbietet, die erste Kontakte zu technischen Arbeitsfeldern her stellen. Auch für bereits berufstätige Ingenieurinnen gibt es zahlreiche Förderprogramme, um den Drop-out hochqualiizierter Frauen auf der Karriere leiter zu verhindern. Dennoch verändern sich die prozentualen Anteile von Frauen in ingenieurswissenschaftlichen Studiengängen und Berufen kaum. Aktuelle Studien belegen, dass vor allem kulturell bedingte Erwartungen und Einstellungen hierfür verantwortlich sind (vgl. Paulitz 2012).
In dieser Arbeit werden neuartige methodische Erweiterungen der Lattice-Boltzmann-Methode (LBM) entwickelt, die effizientere Simulationen inkompressibler Wirbelströmungen ermöglichen. Diese Erweiterungen beheben zwei Hauptprobleme der Standard-LBM: ihre Instabilität in unteraufgelösten turbulenten Simulationen und ihre Beschränkung auf reguläre Rechengitter. Dazu wird zunächst eine Pseudo-Entropische Stabilisierung (PES) entwickelt. Diese kombiniert Ansätze der Multiple-Relaxation-Time (MRT)-Modelle und der Entropischen LBM zu einem expliziten, lokalen und flexiblen Stabilisierungsoperator. Diese Modifikation des Kollisionsschritts erlaubt selbst auf stark unteraufgelösten Gittern stabile und qualitativ korrekte Simulationen. Zur Erweiterung der LBM auf irreguläre Rechengitter wird zunächst eine moderne Discontinuous-Galerkin-LBM untersucht und um stabilere Zeitintegratoren ergänzt. Diese Studie demonstriert die drastischen Schwächen existierender LBMAnsätze auf irregulären Gittern. Basierend auf den gewonnenen Erkenntnissen gelingt die Formulierung einer neuartigen Semi-Lagrangeschen LBM (SLLBM). Diese ermöglicht in einzigartigerWeise sowohl die Verwendung irregulärer Gitter und großer Zeitschritte als auch eine hohe räumliche Konvergenzordnung. Anhand von Beispielsimulationen wird demonstriert, wieso dieser Ansatz anderen aktuellen Off-Lattice-Boltzmann-Methoden (OLBMs) in Effizienz und Genauigkeit überlegen ist. Weitere neuartige Aspekte dieser Arbeit sind die Entwicklung eines modularen Off-Lattice-Boltzmann-Codes und die Erweiterung der LBM um implizite Mehrschrittverfahren, mit denen eine Erhöhung der zeitlichen Konvergenzordnung gelingt.