Universität Stuttgart
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Item Open Access A MATLAB toolbox for the Scintrex CG-5 gravimeter at GIS(2017) Gu, SiyunThis thesis is about a MATLAB toolbox for the Scintrex CG-5 gravimeter. The aim of this toolbox is to offer a basic data process for gravity measurement, which is compatible for most applications in geodesy. In particular, the toolbox covers: 1. data selection, 2. adjustment, 3. gravity gradient computation, 4. gravity visualization, 5. calibration factor estimation. A graphical user interface enables users without deeper programming knowledge to operate this toolbox and obtain the results like adjusted values or figures.Item Open Access Untersuchung der Antriebsstrangdynamik in Windenergieanlagen(2020) Horch, JoachimDiese Arbeit beschäftigt sich damit die Stabilität und Funktionstüchtigkeit des Antriebsstranges einer Windenergieanlage der Größenordnung 10 MW zu untersuchen. Hierfür erfolgt der Aufbau eines Computermodells einer 10-MW-Windenergieanlage mithilfe des Mehrkörpersimulationsprogrammes SIMPACK. Weiterhin wird eine Parameterstudie durchgeführt, welche über eine Matlab-induzierte SIMPACK-Simulation speziell ausgewählte Parameter des Antriebsstranges variiert, Simulationen durchführt und so den Einfluss bestimmter Parameter, sowie Parameterkombinationen, auf die Stabilität des Antriebsstranges prüft. Auf diese Weise sollen Stabilitätskriterien für einen Antriebsstrang dieser Größenordnung ermittelt werden. Es erfolgen sowohl statische, als auch dynamische Untersuchungen.Item Open Access Indefinite linear quadratic optimal control: periodic dissipativity and turnpike properties(2018) Berberich, JulianItem Open Access Validierung eines gekoppelten Simulationsmodells schwimmender Windkraftanlagen mit Hilfe von Modellversuchen(2016) Koch, ChristianFür die Konzeptionierung und Konstruktion schwimmender Windenergiesysteme müssen die Belastungen des Gesamtsystems, die aus kombinierten Wind- und Wellenkräften resultieren, genau untersucht und bestimmt werden. Nur bei genauer Kenntnis dieser Belastungen können effiziente, sichere und wirtschaftliche Gesamtkonzepte entwickelt werden. Für eine zuverlässige Bestimmung der kombinierten Wind- und Wellen- sowie Ankerleinenlasten können validierte Simulationsmodelle eingesetzt werden. Um eine Validierung von Simulationsprogrammen vornehmen zu können, muss auf definierte Lastfälle mit realen Datensätzen zurückgegriffen werden. Im INNWIND.EU Projekt wurde für die Validierung bestehender Simulationscodes eine froudeskalierte, auf der „OC4-DeepCwind“ Halbtaucherplattform basierende, schwimmende 10MW Windenergieanlage mit Rotorblättern mit niedriger Reynoldszahl unter verschiedenen definierten Belastungsfällen in einem kombinierten Wind- und Wellentank in Nantes (Frankreich) untersucht. Im Rahmen dieser Arbeit wird ausgehend von den in in Frankreich erhobenen Daten des INNWIND.EU Projekts ein vollständig gekoppeltes Simulationsmodell, auf einem bestehenden SIMPACK Mehrkörpersimulationsmodell aufgebaut und validiert. Für die Modellierung der Ankerleinenkräfte wurde dabei erstmalig das von NREL entwickelte Ankerleinensimulationsprogramm MAP++ eingesetzt. Die Modellierung der Aerodynamik erfolgte mittels AeroDyn unter Verwendung der Blattelementimpulsmethode. Für die Modellierung der Hydrodynamik wurde HydroDyn mit einer vorgeschalteten AQWA Berechnung zur Bestimmung der hydrodynamischen Koeffizienten eingesetzt. Im Rahmen der Untersuchung wurde eine Vielzahl verschiedener Lastfälle, angefangen von Einschwingversuchen, Versuchen mit reiner Wellen- oder reiner Windbelastung sowie mit kombinierter Wind- und Wellenbelastung simuliert und untersucht. Für die kombinierten Belastungsfälle wurden auch Extrembelastungstests untersucht und die Simulationsergebnisse mit den Messdaten verglichen. Vor allem für große Wellenhöhen zeigten sich dabei gute Übereinstimmungen zwischen Simulation und Messung.Item Open Access The optimal regularization and its application in extreme learning machine for regression analysis and multi-class classification(2018) Qian, KunExtreme Learning Machine (ELM) proposed by Huang et al. (2006) is a newly developed single layer feed-forward neural network (SLFN). It is attractive for its high training efficiency and satisfactory performance, especially when dealing with a large amount of data, which are often in high-dimensional space. However, current ELM cannot solve the over-fitting problem among other several problems. While minimizing residuals of output errors for the training data, it tends to generate an over-fitting model, whose generalization ability is relatively weak. Even if the model fits the training data perfectly, it performs unsatisfactory for the testing data. In training process, we aim to minimize residuals of output errors of training data. It tends to generate an over-fitting model, which has poor generalization ability. The model maybe fit the training data perfectly, but performs badly in testing data. Furthermore, in order to improve accuracy, the traditional way is increasing the number of hidden-layer neurons, but excessive hidden-layer neurons result in an ill-posed normal matrix and a model which is over sensitive to the change of the training data. In such case, the performance of ELM is significantly affected by the outliers in the training data. In order to overcome these problems, we apply the regularization to the original ELM. In this study, the A-optimal design regularization is performed to improve the generalization ability and stability of ELM. The performance of ELM with the A-optimal design regularization will be evaluated through two main applications, respectively, regression analysis and satellite image multi-class classification.Item Open Access GPS time-variable seasonal signals modeling(2015) Chen, QiangSeasonal signals (annual plus semi-annual) in GPS time series are of great importance for understanding the evolution of regional mass, i.e. ice and hydrology. Conventionally these signals (annual and semi-annual) are derived by least-squares fitting of harmonic terms with a constant amplitude and phase. In reality, however, such seasonal signals are modulated, i.e. they will have a time-variable amplitude and phase. Recently, Davis et al. (2012) proposed a Kalman filter based approach to capture the stochastic seasonal behavior of geodetic time series. In this study, a non-parametric approach, singular spectrum analysis (SSA) is introduced. It uses time domain data to extract information from short and noisy time series without prior knowledge of the dynamics affecting the time series. A prominent benefit is that obtained trends are not necessarily linear and extracted oscillations can be amplitude and phase modulated. In this work, the capability of SSA for analyzing time-variable seasonal signals from GPS time series is investigated. We also compare SSA-based results to two model-based results, i.e. least-squares analysis and Kalman filtering. Our results show that singular spectrum analysis could be a viable and complementary tool for exploring modulated oscillations from GPS time series. Based on the SSA-derived seasonal signals, we look into the effects of the input noise variances in the framework of Kalman filtering. Two Kalman filtering based approaches with different process noise models are compared over 79 GPS sites. We find that the basic Kalman filtering technique with the input noise model suggested by Davis et al. (2012) turns out to be optimal.Item Open Access Computational simulation of fluid-structure interaction of soft kites(Stuttgart : University of Stuttgart, Institute of Mechanics, Structural Analysis and Dynamics, 2018) Adam, Niklas JohannesIn order to aid the development and automation of airborne wind energy (AWE) systems, the foundation for fluid-structure interaction (FSI) simulations considering soft kites is developed. FSI simulations are used as a way to predict the deformation of highly flexible structures exposed to a fluid flow and the resulting interaction of solid and fluid. This is especially important for kites since the aeroelastic effects can not be neglected if a realistic approach is regarded. Therefore, the open-source structural multibody dynamics solver MBDyn is coupled to an extension of the open-source computational fluid dynamics (CFD) solver OpenFOAM, namely FOAM-FSI, via the coupling environment preCICE. Relevant modeling features of MBDyn for soft kites such as membrane elements and appropriate boundary conditions are evaluated by means of simple test cases. Furthermore, an adapter for the communication between preCICE and MBDyn is developed and assessed as well. Since an adapter for FOAM-FSI and preCICE already exists, no efforts considering this aspect had to be made. Using this approach, a simple FSI simulation on a ram-air kite section is performed. Due to convincing results regarding the test cases, MBDyn is considered to be a suitable solver for the simulation of soft kites. Moreover, the correct implementation of the adapter is verified by the coupled FSI simulation of a modified benchmark with respect to the aforementioned participating solvers. An approach to FSI simulations on soft kites is successfully developed and verified. However, no reliable final evaluation for the kite section can be made due to the lack of reference solutions.Item Open Access Model predicitve approaches for building climate and seasonal energy storage control(2020) Weber, Simon OskarThe aim of this thesis is the integration of a seasonal energy storage system into the heat supply of a building system under consideration of weather and occupancy forecasts. A thermochemical storage system based on the material system slaked lime / burnt lime is applied as seasonal energy storage. Methodically, a model of an energy system consisting of a building, a water buffer storage tank, a heat pump and a lime storage module is developed. In addition, model-predictive control concepts are developed, which optimally operate the system over a period of one year. For an effective integration of the seasonal lime storage, weather forecasts for an entire year are required. However, public weather forecasts are only considered reliable in the time range of several days. Due to this problem the so-called base year is introduced. The base year data approximate the weather forecast beyond the public forecast period. The weather data of the base year are based on those of the typical meteorological year, which are weather data averaged over several years. On this basis, three model-predictive control concepts are developed. The hierarchy of two concepts provides for a superordinate optimal generation scheduling as well as a subordinate model-predictive control. The optimal generation scheduling uses the disturbance variable inputs of the base year and finds those system inputs through single optimisation which minimise the annual operation costs while keeping all system limits. The resulting lime storage trajectory serves as a reference for the subordinate model predictive control. These concepts now try to follow the lime storage trajectory in an optimal way on the one hand, and on the other hand to realise possible increased or decreased yields due to the public weather data of the current year. The third control concept does not require a superordinate hierarchical level. It uses the public weather forecast for the coming days and the subsequent weather data of the base year to find optimal inputs to the overall system. This thesis demonstrates that the hybrid system consisting of heat pump and lime storage module allows the lowest operating costs for the heat supply of a building. Furthermore, all control concepts presented demonstrate that operation cost savings can be achieved by using the weather forecasts of the base year and integrating the lime storage tank. Depending on the applied configuration of the heat supply, these savings lie between 10% and 30% compared to the model- predictive control, which only includes forecasts over a period of several days and not months.Item Open Access Investigation on the removal of selected organic micropollutants from municipal wastewater by trickling filters and sand filters(2019) Ghorban, ShimaRecently several different types of organic micropollutants are detected in the aquatic environment as a result of inadequate wastewater treatment. The adverse effects of the various micropollutants such as pharmaceuticals, personal care products, pesticides, herbicides and industrial chemicals with concentrations less than 1 μgL-1 on the ecosystem are challenging to be assessed. Thus, sufficient approaches are indispensable to curtail the negative impacts that these substances may have on the environment and human health. Much research was done especially in the recent years on the fate and removal of these emerging contaminants from wastewater by different measures. In this work, a systematic literature review (SLR) is conducted to determine the current state of research in micropollutant removal around the globe which discovers the existing approaches for micropollutant treatment and enables applying an unbiased evaluation. Then one of the identified approaches (sand filter and trickling filter) which is the objective of this study was investigated, and the removal behavior of micropollutants by this method was experimented. The influent and effluent of the trickling filters and sand filters in LFKW wastewater treatment plant were taken and the effect of biodegradation and sorption on the removal of the compounds was investigated. Micropollutant analysis regarding non-polar substances was performed bygas chromatography-mass spectrometry (GCMS) while high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MSMS) was applied instead for polar compounds. Furthermore, the molecular orbital energies of the substances were investigated. UV/Vis spectrophotometry and DOC analysis were other experimental approaches that have been used in order to shed some light on the behavior of these contaminants. As a result of this study, micropollutants are classified in different groups based on their physical-chemical properties, providing it as an essential factor affecting micropollutant removal behavior. Moreover, different correlations between the physical-chemical properties and the micropollutants elimination are assessed.Item Open Access Understanding the limitations of Sentinel-3 inland altimetry through validation over the Rhine River(2022) Schneider, Nicholas M.Satellite altimetry is developing into one of the most powerful measurement techniques for long-term water body monitoring thanks to its high spatial resolution and its increasing level of precision. Although the principle of satellite altimetry is very straightforward, the retrieval of correct water levels remains rather difficult due to various factors. Waveform retracking is an approach to optimize the initially determined range between the satellite and the water body on Earth by exploiting the information within the power-signal of the returned radar pulse to the altimeter. Several so-called retrackers have been designed to this end, yet remain one of the most open study areas in satellite altimetry due to their crucial role they play in water level retrieval. Moreover, geophysical properties of the stratified atmosphere and the target on Earth have an effect on the travel time of the transmitted radar pulse and can amount to severalmeters in range. In this study we provide an overall analysis of the performances of the retrackers dedicated to the Sentinel-3 mission and the applied geophysical corrections. For this matter, we focus on nine different locations within the Rhine River basin where locally gauged data is available to validate the Sentinel-3 level-2 products. Furthermore, we present a reverse retracking approach in the sense that we use the given in-situ data to determine the offset to each altimetry-derived measurement of every epoch. Under the assumption that these offsets are legitimate, they can be seen as an a-posteriori correction which we project onto the range and thus on a waveform level. Further analyses consist in the investigation of the relationship these a-posteriori corrections have to the waveform properties of the same epoch. Later, the question whether the a-posteriori corrections to the initial retracking gates are appropriate for the retrieval of correct water levels, drives us to assign a probability to each and every bin of the waveform. Following this idea, we design stochastic-based retrackers which determine the retracking gate for water level retrieval from the bin with the highest probability assigned to it. To distribute the probabilities across all bins of the waveform, we consider three empirical approaches that take both the waveform itself and its first derivative into account: Addition, multiplication and maximum of both signals. For all three of the new retrackers, we generate the water level timeseries over the aforementioned sites and validate them against in-situ data and the retrackers dedicated to the Sentinel-3 mission.