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 Messungen im Bereich eines Windparks mit Fokus auf tief- und niederfrequente Schallemissionen und -immissionen(2022) Blumendeller, Esther; Gaßner, Laura; Müller, Florian; Wigger, Maayen; Berlinger, Philipp; Cheng, Po WenDie Nutzung von Windenergie wird einen entscheidenden Anteil am erneuerbaren Energiemix der Zukunft haben. Während der Stromgewinnung geben Windenergieanlagen (WEA) Schall und Erschütterungen (seismische Wellen) in die Umgebung ab, vor allem im tieffrequenten Bereich. Im Zuge des interdisziplinären Verbundprojektes Inter-Wind werden akustische Messungen zur Unterstützung psychologischer Fragebögen, kombiniert mit seismischen und meteorologischen Messungen an Windparks auf der Schwäbischen Alb durchgeführt. Ziel des Projektes ist es, die Gründe für Belästigung der Anwohner in Zusammenhang mit den Immissionen der WEA zu verstehen. Hierbei liegt der Fokus auf dem tieffrequenten (20-200 Hz) und niederfrequenten (1-20 Hz) Bereich. Akustische und seismische Messungen wurden an einem Windpark auf der Schwäbischen Alb, mit drei WEA des Typs GE 2.75-120 durchgeführt. Parallel dazu konnten Anwohner Belästigungszeiträume über eine Geräuschmelde-App dokumentieren. In diesem Beitrag wird die Umsetzung einer interdisziplinären Messkampagne im Bereich des Tegelberg Windparks und eines Wohngebäudes in Tallage in ca. 1 km Entfernung zum Windpark beschrieben. Schließlich werden erste Ergebnisse der akustischen Messungen und interdisziplinären Untersuchung vorgestellt und diskutiert.Item Open Access Operational scenarios optimization for resupply of crew and cargo of an International gateway Station located near the Earth-Moon-Lagrangian point-2(2016) Lizy-Destrez, Stéphanie; Messerschmid, Ernst (Prof. Dr.)In the context of future human space exploration missions in the solar system (with an horizon of 2025) and according to the roadmap proposed by ISECG (International Space Exploration Coordination Group) [1], a new step could be to maintain as an outpost, at one of the libration points of the Earth-Moon system, a space station. This would ease access to far destinations as Moon, Mars and asteroids and would allow testing some innovative technologies, before employing them for far distant human missions. One of the main challenges will be to maintain permanently, and ensure on board crew health thanks to an autonomous space medical center docked to the proposed space station, as a Space haven. Then the main problem to solve is to manage the station servitude, during deployment (modules integration) and operational phase. Challenges lie, on a global point of view, in the design of the operational scenarios and, on a local point of view, in trajectories selection, so as to minimize velocity increments (energy consumption) and transportation duration (crew safety). Which recommendations could be found out as far as trajectories optimization is concerned, that would fulfill energy consumption, transportation duration and safety criterion? What would technological hurdles be to rise for the building of such Space haven? What would be performances to aim at for critical sub-systems? Expected results of this study could point out research and development perspectives for human spaceflight missions and above all, in transportation field for long lasting missions. Thus, the thesis project, presented here, aims starting from global system life-cycle decomposition, to identify by phase operational scenario and optimize resupply vehicle mission.Item Open Access Crop water productivity mapping and benchmarking using remote sensing and Google Earth Engine cloud computing(2022) Ghorbanpour, Ali Karbalaye; Kisekka, Isaya; Afshar, Abbas; Hessels, Tim; Taraghi, Mahdi; Hessari, Behzad; Tourian, Mohammad J.; Duan, ZhengScarce water resources present a major hindrance to ensuring food security. Crop water productivity (WP), embraced as one of the Sustainable Development Goals (SDGs), is playing an integral role in the performance-based evaluation of agricultural systems and securing sustainable food production. This study aims at developing a cloud-based model within the Google Earth Engine (GEE) based on Landsat -7 and -8 satellite imagery to facilitate WP mapping at regional scales (30-m resolution) and analyzing the state of the water use efficiency and productivity of the agricultural sector as a means of benchmarking its WP and defining local gaps and targets at spatiotemporal scales. The model was tested in three major agricultural districts in the Lake Urmia Basin (LUB) with respect to five crop types, including irrigated wheat, rainfed wheat, apples, grapes, alfalfa, and sugar beets as the major grown crops. The actual evapotranspiration (ET) was estimated using geeSEBAL based on the Surface Energy Balance Algorithm for Land (SEBAL) methodology, while for crop yield estimations Monteith’s Light Use Efficiency model (LUE) was employed. The results indicate that the WP in the LUB is below its optimum targets, revealing that there is a significant degree of work necessary to ameliorate the WP in the LUB. The WP varies between 0.49-0.55 (kg/m3) for irrigated wheat, 0.27-0.34 for rainfed wheat, 1.7-2.2 for apples, 1.2-1.7 for grapes, 5.5-6.2 for sugar beets, and 0.67-1.08 for alfalfa, which could be potentially increased up to 80%, 150%, 76%, 83%, 55%, and 48%, respectively. The spatial variation of the WP and crop yield makes it feasible to detect the areas with the best and poorest on-farm practices, thereby facilitating the better targeting of resources to bridge the WP gap through water management practices. This study provides important insights into the status and potential of WP with possible worldwide applications at both farm and government levels for policymakers, practitioners, and growers to adopt effective policy guidelines and improve on-farm practices.Item Open Access Analysis of the technical biases of meteor video cameras used in the CILBO system(2017) Albin, Thomas; Koschny, Detlef; Molau, Sirko; Srama, Ralf; Poppe, BjörnIn this paper, we analyse the technical biases of two intensified video cameras, ICC7 and ICC9, of the double-station meteor camera system CILBO (Canary Island Long-Baseline Observatory). This is done to thoroughly understand the effects of the camera systems on the scientific data analysis. We expect a number of errors or biases that come from the system: instrumental errors, algorithmic errors and statistical errors. We analyse different observational properties, in particular the detected meteor magnitudes, apparent velocities, estimated goodness-of-fit of the astrometric measurements with respect to a great circle and the distortion of the camera. We find that, due to a loss of sensitivity towards the edges, the cameras detect only about 55 % of the meteors it could detect if it had a constant sensitivity. This detection efficiency is a function of the apparent meteor velocity. We analyse the optical distortion of the system and the "goodness-of-fit" of individual meteor position measurements relative to a fitted great circle. The astrometric error is dominated by uncertainties in the measurement of the meteor attributed to blooming, distortion of the meteor image and the development of a wake for some meteors. The distortion of the video images can be neglected. We compare the results of the two identical camera systems and find systematic differences. For example, the peak magnitude distribution for ICC9 is shifted by about 0.2–0.4 mag towards fainter magnitudes. This can be explained by the different pointing directions of the cameras. Since both cameras monitor the same volume in the atmosphere roughly between the two islands of Tenerife and La Palma, one camera (ICC7) points towards the west, the other one (ICC9) to the east. In particular, in the morning hours the apex source is close to the field-of-view of ICC9. Thus, these meteors appear slower, increasing the dwell time on a pixel. This is favourable for the detection of a meteor of a given magnitude.Item Open Access Forecasting next year's global land water storage using GRACE data(2024) Li, Fupeng; Kusche, Jürgen; Sneeuw, Nico; Siebert, Stefan; Gerdener, Helena; Wang, Zhengtao; Chao, Nengfang; Chen, Gang; Tian, KunjunExisting approaches for predicting total water storage (TWS) rely on land surface or hydrological models using meteorological forcing data. Yet, such models are more adept at predicting specific water compartments, such as soil moisture, rather than others, which consequently impedes accurately forecasting of TWS. Here we show that machine learning can be used to uncover relations between nonseasonal terms of Gravity Recovery and Climate Experiment (GRACE) derived total water storage and the preceding hydrometeorological drivers, and these relations can subsequently be used to predict water storage up to 12 months ahead, and even exceptional droughts on the basis of near real‐time observational forcing data. Validation by actual GRACE observations suggests that the method developed here has the capability to forecast trends in global land water storage for the following year. If applied in early warning systems, these predictions would better inform decision‐makers to improve current drought and water resource management.