06 Fakultät Luft- und Raumfahrttechnik und Geodäsie

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7

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2015 - 2019172020 - 20229

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Publication Type: search.filters.UBSTyp.Abschlussarbeit (Master)Subject: search.filters.subject.620

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    A MATLAB toolbox for the Scintrex CG-5 gravimeter at GIS
    (2017) Gu, Siyun
    This 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.
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    Monitoring inland surface water level from Sentinel-3 data
    (2019) Wang, Bo
    Inland surface water bodies (e.g. lakes and rivers) are very important to the nature and human society. To monitor the water level of inland water bodies, gauge stations were built since 19th century, but the amount of the stations is declining since the 1970s because of lack of maintenance. An accurate and continuous monitoring of lakes and rivers is available because of the satellite altimetry missions launched, e.g. Jason-2 and ENVISAT. These satellites can provide water level with proper spatial and temporal resolution. In the recent past, researchers have used different satellite mission observations to generate time series of inland water level in order for monitoring the water bodies. In this thesis, we use the new designed satellite mission Sentinel-3, which carries different sensors, to generate the water level time series of Dongting Lake and Poyang Lake in China. Initially, we combine the altimetry measurements with satellite images to determine virtual station. We choose Sentinel-3 Ku band data and on-board Ocean tracker to generate the water level time series. Afterwards, we apply different waveform retracking algorithms (5β-parameter and OCOG) to compare the results with on-board tracker. We also validate the results with the other database, then investigate the waveforms of each sampling date. The comparisons show the three tracking methods we used are capable to Quasi-Specular waveforms, and OCOG shows the best result to flat patch waveforms. Furthermore, some suggestions for improvements are also discussed in the last chapter.
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    Characterizing storage-based drought using satellite gravimetry
    (2021) Saemian, Peyman
    Drought is a complex phenomenon leading to a wide range of socio-economic, environmental, and political problems. The storage-based drought which represents the persistent lack of water in different levels of the Total Water Storage (TWS) from deep groundwater to surface water plays a vital role in proactive drought management. Despite its necessity, TWS could not be monitored due to the lack of consistent measurements from regional to continental scale. Since its launch in 2002, the Gravity Recovery and Climate Experiment (grace) mission and its successor GRACE Follow-On have provided unique observations of the TWS change at the global scale. In this study, we have investigated characterizing the storage-based drought at the global scale using GRACE measurements. To this end, the Equivalent Water Height (EWH) has been retrieved from GRACE level 02 solutions. We have addressed the short record of GRACE observations in capturing the full hydroclimate variations. Based on our analysis, regions with a considerable direct human intervention like overexploitation of groundwater in the Middle East, regions that were affected by climate change like ice-melting over the Mackenzie river basin in Canada, or extreme precipitation events over the Ob river basin in the boreal regions are more sensitive to the length of ewh time series. Due to the crucial need for a long (at least 30 years) record of EWH, we have extended GRACE observations back to 1980 using an ensemble of models. The extended dataset has been developed using a pixel-wise selection of best-performed models among global hydrological models, land surface models, and atmospheric reanalysis models. The extended dataset has been used in the study for drought characterization over the grac period. The proposed Storage-based Drought Index (SDI) successfully captured the documented drought events globally in terms of intensity and spatio-temporal distribution. Moreover, the analysis of SDI over the five classes of drought from D0 as abnormally dry to D4 as exceptional drought showed that most regions have suffered at least once from the storage-based drought over the GRACE period (2002–2016). Besides, the map of exceptional drought frequency highlights regions with significant groundwater extraction like California, the Middle East, and north of India and regions with exceptional shifts in the precipitation and temperature pattern and intensity like Amazon in South America and China. Finally, our comparison of SDI with three most widely used drought indices namely the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Palmer Drought Severity Index (PDSI) reveals that despite their high correlation over climate-driven regions, these indices failed to characterize anthropogenic drought events, especially over regions with considerable groundwater withdraws. The study allows for a more informative storage-based drought with a more robust climatology as the reference, thus enabling a more realistic risk assessment.
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    Assessment of altimetric river water level time series densification methods
    (2018) Xia, Zhuge
    Nowadays, collecting and analysing water level time series recorded by gauging stations or by satellite altimetry is crucial for the geodetic and environmental purposes, such as modelling ocean circulation and monitoring climate change. Since the 1970s, a large number of gauging stations has been removed. This has made altimetry increasing more important. However, data collected by individual altimetric satellites are limited, i.e., the temporal resolution is limited to the repeat cycle of satellites, and the spatial resolution is constrained to the distribution of virtual stations. In order to overcome these limitations, methods have been developed to combine all available altimetric satellite missions along a river to construct a new densified time series. This is referred to as densification. To our knowledge, there are only two proven densification methods applied to the river for now. The first is a hydraulic statistic densification method developed by Tourian et al. (2016). The other is the kriging densification method published by Boergens et al. (2017). However, each of them is realized under different circumstances, which makes them incomparable with each other. In this work, we implement the two densification methods and apply them under similar conditions. The various densified water level time series are compared and analysed both visually and statistically. Results reveal different characteristics of the two densification methods.
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    Validierung eines gekoppelten Simulationsmodells schwimmender Windkraftanlagen mit Hilfe von Modellversuchen
    (2016) Koch, Christian
    Für 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.
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    The optimal regularization and its application in extreme learning machine for regression analysis and multi-class classification
    (2018) Qian, Kun
    Extreme 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.
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    GPS time-variable seasonal signals modeling
    (2015) Chen, Qiang
    Seasonal 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.
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    Computational simulation of fluid-structure interaction of soft kites
    (Stuttgart : University of Stuttgart, Institute of Mechanics, Structural Analysis and Dynamics, 2018) Adam, Niklas Johannes
    In 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.
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    Evaluate the performance of a camber controlled cycloidal rotor
    (2022) Huang, Doudou
    The curvature of the airfoil has a significant effect on the performance of the cycloidal rotor system. This paper aims to improve the aerodynamic performance of the cycloidal rotor system by utilizing dynamical morphing blades in a CFD model. Particularly, three different camber morphing concepts, including leading edge deflection, trailing edge deformation, and cambered NACA profile, are employed to a baseline 2-bladed system with rotating and pitching NACA0015 aerofoils. Based on these three camber concepts, a series of URANS 2-D numerical simulations in OpenFOAM are conducted for blades with different morphing degrees and positions. The simulation results verified that the flow field condition could be optimized and thus significant improvement in thrust and efficiency could be achieved by properly tuning the morphing control.
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    Analysing normal modes of the earth from high-rate GNSS time series
    (2017) Chen, Zhongyi
    Normal modes of the Earth, or Earth’s free oscillations, correspond to a global deformation of the Earth that vibrates at different frequencies, like a bell, after a strong excitation, usually an earthquake of magnitude greater than 6.5. Normal modes of the Earth were first described by Lord Kelvin (Kelvin, 1863) with a computation of the lowest fundamental spheroidal mode 0S2 frequency for a homogeneous Earth model (Lognonné and Clévédé, 2002). With the theory and the deployment of the first long-period sensors in the late 1950s, day-scale Earth’s free oscillation after large earthquakes has been detected by underground instruments such as strainmeters, gravimeters and seismometers (Benioff et al., 1961) (Dziewonski and Gilbert, 1972) (Mendiguren, 1973). In the 1960s, since the U.S. military developed the first satellite navigation system, Transit, the era of Global Navigation Satellite System (GNSS) has arrived. Among all navigation satellite systems, Global Positioning System (GPS), operated by the U.S. Department of Defense (DOD), is currently the world’s most utilized satellite navigation system. With the developments of receiver technology and sampling capability, GPS becomes a powerful tool to study long-period Earth deformations such as plate tectonics and post-glacial rebound, or to monitoring short-period and short-duration motion such as waves generated by earthquakes (Bilich et al., 2008). In recent years, several studies have demonstrated the effective use of GPS in estimating coseismic displacement waveforms induced by an earthquake with accuracies ranging from a few millimeters to a few centimeters. In these studies, two well-known processing strategies, single Precise Point Positioning (PPP) and Different Positioning (DP), have been used to reduce the latency between earthquake occurrence and coseimic displacement waveforms estimation. In this thesis, a new approach named Variometric Approach for Displacements Analysis Standalone Engine (VADASE) is used to detect the normal modes of the Earth. Then the Welch’s PSD estimate is applied to transform the time series into frequency domain. Several simulations have been performed on synthetic time series to investigate the influence of noise level, sampling rate, time series length, window size and overlapping rate of Welch’s method, as well as the influence of stacking. The experiments on real data show the capability of VADASE time series for detecting normal modes of the Earth with the help of the stacking method. Some fundamental modes with small amplitude are not visible because the SNR is not sufficient to lift the signal out of the noise.