06 Fakultät Luft- und Raumfahrttechnik und Geodäsie
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7
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Item Open Access Berechnung und Bewertung der Schallausbreitung von Windenergieanlagen mit Hilfe von Messdaten(2023) Häfele, Lorenz KarlItem Open Access Control co-design optimization of floating offshore wind turbines with tuned liquid multi-column dampers(2024) Yu, Wei; Zhou, Sheng Tao; Lemmer, Frank; Cheng, Po WenThe technical progress in the development and industrialization of floating offshore wind turbines (FOWTs) over the past decade has been significant. Yet, the higher levelized cost of energy (LCOE) of FOWTs compared to onshore wind turbines is still limiting the market share. One of the reasons for this is the larger motions and loads caused by the rough environmental excitations. Many prototype projects tend to employ more conservative substructure designs to meet the requirements for motion dynamics and structural safety. Another challenge lies in the multidisciplinary nature of a FOWT system, which consists of several strongly coupled subsystems. If these subsystems cannot work in synergy, the overall system performance may not be optimized. Previous research has shown that a well-designed blade pitch controller is able to reduce the motions and structural loads of FOWTs. Nevertheless, due to the negative aerodynamic damping effect, improvement in the performance by tuning the controller is limited. One of the solutions is adding tuned liquid multi-column dampers (TLMCDs), meaning that there is a structural solution to mitigate this limiting factor for the controller performance. It has been found that the additional damping, provided by TLMCDs, is able to improve the platform pitch stability, which allows a larger blade pitch controller bandwidth and thus a better dynamic response. However, if a TLMCD is not designed with the whole FOWT system dynamics taken into account, it may even deteriorate the overall performance. Essentially, an integrated optimization of these subsystems is needed. For this paper, we develop a control co-design optimization framework for FOWTs installed with TLMCDs. Using the multi-objective optimizer non-dominated sorting genetic algorithm II (NSGA-II), the objective is to optimize the platform, the blade pitch controller, and the TLMCD simultaneously. Five free variables characterizing these subsystems are selected, and the objective function includes the FOWT's volume of displaced water (displacement) and several motion and load indicators. Instead of searching for a unique optimal design, an optimal Pareto surface of the defined objectives is determined. It has been found that the optimization is able to improve the dynamic performance of the FOWT, which is quantified by motions and loads, when the displacement remains similar. On the other hand, if motions and loads are constant, the displacement of the FOWT can be reduced, which is an important indication of lower manufacturing, transportation, and installation costs. In conclusion, this work demonstrates the potential of advanced technologies such as TLMCDs to advance FOWTs for commercial competitiveness.Item Open Access Quantification of amplitude modulation of wind turbine emissions from acoustic and ground motion recordings(2023) Blumendeller, Esther; Gaßner, Laura; Müller, Florian J. Y.; Pohl, Johannes; Hübner, Gundula; Ritter, Joachim; Cheng, Po WenItem Open Access A probabilistic approach to characterizing drought using satellite gravimetry(2024) Saemian, Peyman; Tourian, Mohammad J.; Elmi, Omid; Sneeuw, Nico; AghaKouchak, AmirIn the recent past, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor GRACE Follow‐On (GRACE‐FO), have become invaluable tools for characterizing drought through measurements of Total Water Storage Anomaly (TWSA). However, the existing approaches have often overlooked the uncertainties in TWSA that stem from GRACE orbit configuration, background models, and intrinsic data errors. Here we introduce a fresh view on this problem which incorporates the uncertainties in the data: the Probabilistic Storage‐based Drought Index (PSDI). Our method leverages Monte Carlo simulations to yield realistic realizations for the stochastic process of the TWSA time series. These realizations depict a range of plausible drought scenarios that later on are used to characterize drought. This approach provides probability for each drought category instead of selecting a single final category at each epoch. We have compared PSDI with the deterministic approach (Storage‐based Drought Index, SDI) over major global basins. Our results show that the deterministic approach often leans toward an overestimation of storage‐based drought severity. Furthermore, we scrutinize the performance of PSDI across diverse hydrologic events, spanning continents from the United States to Europe, the Middle East, Southern Africa, South America, and Australia. In each case, PSDI emerges as a reliable indicator for characterizing drought conditions, providing a more comprehensive perspective than conventional deterministic indices. In contrast to the common deterministic view, our probabilistic approach provides a more realistic characterization of the TWS drought, making it more suited for adaptive strategies and realistic risk management.Item Open Access Surface water extent monitoring using the Global WaterPack product: automated extraction, refinement, and analysis(2025) Jalali Jirandehi, MasoudMonitoring and analyzing surface water dynamics is critical for understanding hydrological variations, climate change impacts, and water resource management. Traditional methods of surface water storage monitoring rely on in-situ measurements, which are often spatially and temporally limited. Remote sensing has revolutionized this field by enabling large-scale, consistent, and continuous observations of area and height water storages. This study presents a methodology for generating accurate water area time series using the Global WaterPack (GWP), a monthly satellite-derived dataset. Developed by the German Aerospace Center (DLR), the GWP dataset is specifically designed for monitoring surface water dynamics on a global scale. A Python-based processing tool is developed to systematically extract and analyze lake and river water extents, mitigating key challenges such as cloud contamination, defining proper threshold, and classification inaccuracies. By integrating high-frequency surface water observations from GWP with the Prior Lake Database PLD (a static dataset for extracting the initial search area), and the SWOT (The Surface Water and Ocean Topography) prior River Database (SWORD), which provides a standardized framework of high-resolution river nodes and reaches, this tool enhances the reliability of time-series analysis. This framework improves surface water change detection, reduces computational complexity, and refines water occurrence assessments under diverse hydroclimatic conditions. The workflow automates the entire process, allowing users to select lakes interactively via a geospatial interface or upload coordinate lists for batch processing. Key steps include (1) downloading images, (2) defining the search area, (3) normalization, (4) generating a water occurrence map, (5) defining constant water and land masks, (6) residual analysis, (7) deriving and applying thresholds, (8) generating time series plots, and (9) correlation analysis. Advanced filtering methods, such as threshold-based classification and residual analysis, refine water occurrence measurements, while an adaptive thresholding approach using the Cumulative Distribution Function (CDF) enhances water body delineation accuracy. To evaluate the reliability of the extracted data, the resulting surface area time series are compared against altimetry-derived water height records using correlation analysis. The analysis revealed clear seasonal and interannual variations in lake water areas, aligning well with natural hydrological patterns. Many lakes showed strong positive correlations between satellite-derived surface area and altimetry-based water levels, validating the method’s effectiveness. However, weaker correlations in some cases were attributed to issues like cloud cover, sensor limitations, and complex hydrodynamics. The study emphasized that a fixed threshold is insufficient for all systems, whereas the corrected method provided more reliable results across diverse conditions. Although river analysis showed varied hydraulic responses, the tool proved useful for monitoring floods, seasonal changes, and long-term water trends, especially with proper calibration. By providing an automated, scalable, and accurate tool for water body monitoring, this thesis contributes to advancing hydrological analysis using remote sensing and geospatial processing techniques. The developed tool can aid in climate studies, water resource management, and flood risk assessment, offering a valuable framework for long-term surface water monitoring.Item Open Access Affine transformations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem(2023) Mandl, Luis; Mielke, André; Seyedpour, Seyed Morteza; Ricken, TimPhysics-informed neural networks (PINNs) leverage data and knowledge about a problem. They provide a nonnumerical pathway to solving partial differential equations by expressing the field solution as an artificial neural network. This approach has been applied successfully to various types of differential equations. A major area of research on PINNs is the application to coupled partial differential equations in particular, and a general breakthrough is still lacking. In coupled equations, the optimization operates in a critical conflict between boundary conditions and the underlying equations, which often requires either many iterations or complex schemes to avoid trivial solutions and to achieve convergence. We provide empirical evidence for the mitigation of bad initial conditioning in PINNs for solving one-dimensional consolidation problems of porous media through the introduction of affine transformations after the classical output layer of artificial neural network architectures, effectively accelerating the training process. These affine physics-informed neural networks (AfPINNs) then produce nontrivial and accurate field solutions even in parameter spaces with diverging orders of magnitude. On average, AfPINNs show the ability to improve the L2relative error by 64.84%after 25,000 epochs for a one-dimensional consolidation problem based on Biot’s theory, and an average improvement by 58.80%with a transfer approach to the theory of porous media.Item Open Access Investigating 3-D effects on flashing cryogenic jets with highly resolved LES(2023) Gärtner, Jan Wilhelm; Kronenburg, Andreas; Rees, Andreas; Oschwald, MichaelFor the development of upper stage rocket engines with laser ignition, the transition of oxidizer and fuel from the pure cryogenic liquid streams to an ignitable mixture needs to be better understood. Due to the near vacuum conditions that are present at high altitudes and in space, the injected fuel rapidly atomizes in a so-called flash boiling process. To investigate the behavior of flashing cryogenic jets under the relevant conditions, experiments of liquid nitrogen have been performed at the DLR Lampoldshausen. The experiments are accompanied by a series of computer simulations and here we use a highly resolved LES to identify 3D effects and to better interpret results from the experiments and existing 2D RANS. It is observed that the vapor generation inside the injector and the evolution of the spray in the combustion chamber differ significantly between the two simulation types due to missing 3D effects and the difference in resolution of turbulent structures. Still, the observed 3D spray dynamics suggest a suitable location for laser ignition that could be found in regions of relative low velocity and therefore expected low strain rates. Further, measured droplet velocities are compared to the velocities of notional Lagrangian particles with similar inertia as the measured droplets. Good agreement between experiments and simulations exists and strong correlation between droplet size and velocity can be demonstrated.Item Open Access Evaluation of gravitational curvatures for a tesseroid and spherical shell with arbitrary-order polynomial density(2023) Deng, Xiao-LeIn recent years, there are research trends from constant to variable density and low-order to high-order gravitational potential gradients in gravity field modeling. Under the research circumstances, this paper focuses on the variable density model for gravitational curvatures (or gravity curvatures, third-order derivatives of gravitational potential) of a tesseroid and spherical shell in the spatial domain of gravity field modeling. In this contribution, the general formula of the gravitational curvatures of a tesseroid with arbitrary-order polynomial density is derived. The general expressions for gravitational effects up to the gravitational curvatures of a spherical shell with arbitrary-order polynomial density are derived when the computation point is located above, inside, and below the spherical shell. When the computation point is located above the spherical shell, the general expressions for the mass of a spherical shell and the relation between the radial gravitational effects up to arbitrary-order and the mass of a spherical shell with arbitrary-order polynomial density are derived. The influence of the computation point’s height and latitude on gravitational curvatures with the polynomial density up to fourth-order is numerically investigated using tesseroids to discretize a spherical shell. Numerical results reveal that the near-zone problem exists for the fourth-order polynomial density of the gravitational curvatures, i.e., relative errors in log10scale of gravitational curvatures are large than 0 below the height of about 50 km by a grid size of 15′×15′. The polar-singularity problem does not occur for the gravitational curvatures with polynomial density up to fourth-order because of the Cartesian integral kernels of the tesseroid. The density variation can be revealed in the absolute errors as the superposition effects of Laplace parameters of gravitational curvatures other than the relative errors. The derived expressions are examples of the high-order gravitational potential gradients of the mass body with variable density in the spatial domain, which will provide the theoretical basis for future applications of gravity field modeling in geodesy and geophysics.