20005 research outputs found
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How shallow and deep groundwater impact environmental parameters correlated with global heatwaves
No full textHeatwaves present serious challenges to ecosystems, human health, and a wide range of socioeconomic activities. As the frequency and intensity of heatwaves increase, understanding the mechanisms driving their dynamics and interactions with land surface processes become more important. While extensive research has investigated the influence of various land and atmospheric parameters on heatwaves, less is known about how groundwater depth influences heatwave dynamics through their effects on soil moisture and surface evaporative fluxes (Vogelbacher et al., 2024, Sadeghi et al., 2012). To address this knowledge gap, we investigated how the groundwater depth affects the key parameters controlling heatwave dynamics on a global scale. Specifically, we developed more than 200,000 localized Artificial Intelligence (AI) models to represent the spatial distribution of heatwave frequency over the past 21 years across the world. For each model, a radius of 1.5 degrees (approximately 149 neighboring pixels) is considered in the computation to identify key parameters contributing to heatwaves in that region. We analyzed surface fluxes, as well as atmospheric, hydrological, and local environmental variables, to understand their correlation to heatwaves. Our findings suggest that geopotential height representing atmospheric drivers, is the key predictor of heatwave events in regions with deep groundwater tables (>100 m). In contrast, in areas with shallow groundwater (<10 m), surface fluxes emerge as important contributor to the onset of heatwaves. These findings highlight the less-discussed impact of groundwater depth on atmospheric processes and the important role of soil in linking groundwater and the atmosphere. Our results have important implications for water and land management, emphasizing the need for integrated approaches to understand and address the increasing risks posed by heatwaves.Deutsche Forschungsgemeinschaft (DFG
Do mobility hubs boost the adoption and impact of carsharing in the urban periphery? Insights from a mixed-methods case study
No full textMobility hubs represent an emerging instrument in transportation planning, with the potential to catalyze the uptake and effects of carsharing. However, extant research on the interplay between mobility hubs and carsharing is limited and has focused predominantly on highly urbanized areas. The question of how mobility hubs and carsharing interact in the urban periphery, where conditions for carsharing are considerably less favorable, remains largely unexplored in the literature. In this context, our paper seeks to answer whether mobility hubs enhance the adoption and impact of carsharing on the outskirts of a major city. Utilizing newly established mobility hubs in southern Hamburg, Germany, as a case study, our analysis draws on 14 interviews with residents
living near these hubs and a survey of the local population (n = 310). Our findings indicate that peripheral mobility hubs have a positive influence on the adoption of carsharing and the willingness of carsharing users to forgo car ownership. This phenomenon can be attributed to the heightened visibility of carsharing services and the enhanced accessibility of carsharing vehicles and parking spaces. However, the efficacy of the mobility hubs was hindered by multiple factors, including hub-related issues (e.g., parking spaces occupied by unauthorized vehicles) and systemic deficiencies in carsharing services in peripheral areas (e.g., insufficient vehicle availability or unattractive pricing for trips across the city limits). Consequently, while mobility hubs in the urban periphery demonstrate capability in fostering carsharing, their impact remains constrained in the absence of comprehensive measures to improve carsharing offerings in these areas
Bearing capacity of tension steel piles in thinly inter-layered soils: numerical Class-A prediction vs. field measurements
No full textThis study investigates the bearing capacity of tension piles and pile-soil interaction during loading. Discrepancies between predicted and measured bearing capacities in previous tests motivated the study, where analytical methods showed considerable scatter and uncertainties in design. A large-scale field test was conducted on three additional adjacent tension piles (Pile 1, 2 and 3), featuring extensive fibre-optic strain measurements. Concurrently, a numerical Class-A prediction was developed beforehand to analyse pile-soil interaction and predict bearing capacity, utilising hypoplastic and visco-hypoplastic models for the thinly inter-layered subsoils. The fibre-optic measurements revealed significant locked-in bending strains post-installation, prior to loading. The results showed a correlation between pronounced bending strains and lower load-bearing capacity. Numerical predictions were compared with the field measurements, providing good agreement with Pile 1, which exhibited minimal installation-induced bending and thus represented an idealised case. This comparison offered valuable insights into tension pile failure mechanisms and load capacity. This research enhances understanding of tension pile behaviour in complex soils and underscores the necessity of optimising installation methods to improve load-bearing capacities
Die „Designanalyse“ als gestaltungsorientiertes Unterrichtsverfahren
No full textDie Designanalyse ist ein Ausbildungs- und Unterrichtsverfahren, das Lernende befähigt, Entwurfs- und Produktmerkmale systematisch zu erfassen, zu bewerten und für Gestaltungsprozesse zu nutzen. In der beruflichen Bildung dient die Designanalyse sowohl der Vorbereitung als auch der Auswertung von Designaufgaben und verbindet analytische mit kreativen Prozessen. Am Beispiel von Designfunktionen wird verdeutlicht, wie Analyseergebnisse in handlungsorientierten Lernsituationen weitergeführt werden können
Innovationen bei der Entwicklung und Optimierung von Leichtbaustrukturen
No full textDieses Kapitel untersucht Innovationen in der Entwicklung und Optimierung von Leichtbaustrukturen mit Fokus auf quasistatische und dynamische Lasten in Test und Simulation. Verschiedene Optimierungsansätze werden hinsichtlich ihrer Anwendbarkeit bei variantenreichen Produktfamilien evaluiert. Zudem wird der Einsatz von Faserverbundwerkstoffen in unterschiedlichen Einsatzbereichen untersucht
Experimental open-source framework for underwater pick-and-place studies with lightweight UVMS – an extensive quantitative analysis
No full textThe rise of lightweight, low-cost underwater vehicle-manipulator systems (UVMS) has made autonomous underwater manipulation increasingly accessible. Yet, most current research remains limited to isolated tasks, such as trajectory tracking or compensation of unknown payloads. Detailed experimental analyses that go beyond a proof-of-concept are particularly rare.We present a comprehensive open-source software framework for fully automated pick-and-place studies. We build upon our previous work on a task-priority control framework and extend it to enable fully autonomous manipulation. This includes a high-level decision-making process to coordinate the pick-and-place sequence and a grasp detection method to verify the successful pick-up of the object. We demonstrate this framework on the widely-used platform of a BlueROV2 and an Alpha 5 manipulator.Extensive quantitative experimental studies (100+ trials) show the picking and placing to be highly accurate, with mean position errors of <5 mm and <10 mm, respectively. We additionally validate our grasp detection approach and analyze trajectory tracking sensitivity to varying payloads and speeds. These results provide a baseline of what accuracy is currently achievable with state-of-the-art lightweight hardware under ideal research conditions. The code is available at https://github.com/HippoCampusRobotics/uvms
Data-driven system modelling in the system generation engineering
No full textThis work develops a method to integrate operational data into system models following MBSE principles. Empirical analysis reveals significant obstacles to data-driven development, including heterogeneous and non-transparent data structures, poor metadata documentation, insufficient data quality, lack of references, and limited data-driven mindset. A method based on the RFLP chain links operating data structures to logical-level elements. Data analyses are aligned with specific requirements or functional/physical elements, enabling systematic data-driven modeling. This method improves efficiency, fosters system knowledge development, and connects technical systems with operational data
Automated defect detection in fused filament fabrication coupling deep learning and computer vision
No full textFused filament fabrication (FFF) is an additive manufacturing technique, popular due to its versatility and cost-effectiveness. However, FFF machines, such as 3D printers, are prone to runtime errors, wasting time and material, while requiring constant human supervision. This paper presents a defect detection approach for FFF processes based on artificial intelligence, combining convolutional neural networks and computer vision. The defect detection approach is validated using 3D prints designed to mimic common FFF defects. The results demonstrate the capability of the proposed approach to automatically detect defects in FFF processes, thereby reducing time and material waste as well as the need for human supervision
A lattice Boltzmann approach for modeling coupled evaporation/precipitation processes in porous media
No full textWe present a novel numerical approach for simulating coupled evaporation and salt precipitation processes in porous media using the lattice Boltzmann method. Our model combines a Shan-Chen multiphase flow framework with a volume-based discretization method for diffusing salt species, where the coupling mechanism operates through solvation energy differences. The framework incorporates a precipitation model based on first-order reaction kinetics, enabling the transformation of fluid cells into solid crystal cells upon reaching threshold concentrations. We verify our model against analytical solutions for crystal growth and evaporation rates, demonstrating excellent agreement. The model is then applied to investigate the influence of wetting properties on evaporation and salt precipitation in porous media. Our simulations reveal that the wettability of both the porous medium and the precipitated salt crystals has a significant impact on precipitation patterns and pore clogging. Notably, increased salt crystal wettability promotes more effective pore blocking, substantially reducing permeability and evaporation rates. We find that the slowest evaporation occurs when salt crystals have high wettability, while the porous medium exhibits lower wettability because this configuration maximizes pore clogging. This work presents a novel framework to gain insight into the intricate interplay between fluid dynamics, wetting properties, and salt precipitation in porous media, with implications for understanding soil salinization, building material degradation, and the formation of geological salt crusts
Dickman approximation of weighted sums of independent random variables in the Kolmogorov distance
No full textWe consider distributional approximation by generalized Dickman distributions, which appear in number theory, perpetuities, logarithmic combinatorial structures and many other areas. We prove bounds in the Kolmogorov distance for the approximation of certain weighted sums of Bernoulli and Poisson random variables by members of this family.While such results have previously been shown in Bhattacharjee and Goldstein (2019) for distances based on smoother test functions and for a special case of the random variables considered in this paper, results in the Kolmogorov distance are new. We also establish optimality of our rates of convergence by deriving lower bounds. As a result, some interesting phase transitions emerge depending on the choice of the underlying parameters. The proofs of our results mainly rely on the use of Stein’s method. In particular, we study the solutions of the Stein equation corresponding to the test functions associated to the Kolmogorov distance, and establish their smoothness properties. As applications, we study the runtime of the Quickselect algorithm, an edge-length statistic of a long-range percolation model, and the weighted depth in randomly grown simple increasing trees