20005 research outputs found
Sort by
Impact of sulfate on the release of genotoxic metals from hardened cement pastes
No full textWe investigated the effects of environmentally relevant sulfate concentrations on the leaching behavior of certain metalloids in hardened cement pastes. In our study, different cement pastes made of Portland cement (CEM I), blast furnace slag cement (CEM III/A) and a sulfate-resistant cement (CEM I SR0) were cured for 28 days and leached with ultrapure water and with sulfate-containing water. The released concentrations of the most metals and metalloids were independent of the presence of environmentally relevant sulfate concentrations below 1 µg/L or even below the limit of quantification (LOQ). However, the contact to sulfate-containing water led to an increased chromium release from CEM I, compared to leaching in ultrapure water. Under the same conditions an increased release of vanadium was observed from CEM III/A. A micronucleus test of the selected eluates revealed genotoxic effects which can be very likely attributed to the presence of vanadate. We were further able to connect the different leaching behavior of cement in sulfate-containing water compared to ultrapure water to changes of the specific surface area of the hardened cement pastes
Comparative analysis of local trajectory planning algorithms in ROS2
No full textLocal path planners are essential for real-time obstacle avoidance, particularly in unpredictable industrial environments. This study compares three ROS 2 local path-planning algorithms: Dynamic Window Approach (DWB), Model Predictive Path Integral (MPPI), and Regulated Pure Pursuit (RPP). These planners are part of the Robot Operating System 2 (ROS 2) navigation stack (Nav2), with DWB and MPPI as built-in controllers, and RPP integrated as a controller option.While previous studies have focused on simulations or simple real-world environments with static obstacles, few have examined dynamic and unpredictable scenarios. To address this, experiments were conducted with a Clearpath Jackal robot in environments with static and dynamic obstacles. Key metrics, including distance to obstacles, frequency of recovery behaviors, and path smoothness, were used to compare the planners performance in both real-world and simulated settings.MPPI offers a strong balance across all metrics, making it ideal for dynamic environments requiring both safety and efficiency. DWB excels in fast navigation, though with closer proximity to obstacles, while RPP produces smooth paths but struggles in highly dynamic environments. These results provide valuable insights for selecting the right local path planner for autonomous robots in industrial applications like manufacturing and warehousing
Physics-informed machine learning for virtual inertia provision from power distribution systems
No full textGreen Hydrogen Supply Chain Network Design for Aviation: Model Development and Case Study for German Airports in 2050
No full textHydrogen-based propulsion concepts for aircraft are considered a promising technology towards the decarbonization of aviation. While the development of respective aircraft models is in progress, questions regarding the supply network of green hydrogen are arising. We present a formulation of the hydrogen supply chain network (HSCN) design problem that focuses on the aviation sector. The mixed-integer linear programming model minimizes the total cost of hydrogen supply by deciding on locations, capacities, transportation modes and lows. The respective supply chain starts with the generation of renewable electricity used in the electrolysis of water. The gas hydrogen obtained from this process is liquefied before being used to refuel the aircraft. Moreover, various transportation and storage processes for gas and liquid hydrogen are involved between the electrolyzers and the airports. Our model formulation considers the spatially dispersed availability of renewable electricity, the techno-economic characteristics of hydrogen storage, liquefaction and transportation (e.g., economies of scale), as well as the specific requirements of hydrogen handling (e.g., losses). The application is illustrated for Germany in 2050, considering the hydrogen pipeline backbone projection. Optimal network design and results are presented
Effects of nucleotomy on segmental flexibility: a numerical analysis
No full textNucleotomy, a common treatment for disc herniations, aims to relieve pressure on spinal structures. While effective in alleviating symptoms, this intervention can compromise spinal stability. However, previous in vivo studies in sheep have demonstrated conflicting results with significant long-term stiffening of the spine following nucleotomy, with occasional spontaneous fusion of the affected motion segment. The objective of this study was to investigate the mechanical regulation of tissue adaptation processes post-nucleotomy using computational modeling. A parametric finite element model of the L4–L5 ovine spinal motion segment, developed previously, was modified to simulate surgical procedures that have been performed in prior in vivo studies. An iterative approach was used to simulate post-surgical tissue healing and adaptation processes. Two loading scenarios were simulated: one with combined axial compression and flexion moments, and the other incorporating axial rotation. An initial decrease in stability, with stiffness reduced by up to 50% due to disc decompression and nucleus removal, was followed by a gradual increase in stiffness over time as a consequence of bone healing and remodeling, with the most pronounced stiffening – up to 350% of the intact state – observed in axial rotation. The findings align with previous in vivo observations, suggesting that spontaneous fusion and increased rigidity may be natural consequences of mechano-biological adaptation. The results of this study highlight that healing processes accompanied by adaptive bone remodeling are directed towards restoration of spinal stability after nucleotomy. These findings align with previous in vivo observations, suggesting that spontaneous fusion and increased rigidity may be a natural consequence of post-nucleotomy mechano-biological adaptation. On the other hand, the results indicate a critical role of an appropriate loading regime on the outcome of these processes
Automated construction progress monitoring coupling lidar inertial odometry and building information modeling
No full textBuilding information modeling (BIM) models contain valuable information for mobile robots deployed to increase the efficiency of automated building inspections. To access the information, robots require accurate localization relative to BIM models. Since BIM models represent the "as-planned" rather than the "as-built" state, localization is challenging due to deviations between BIM models and the reality. In this paper, lidar inertial odometry (LIO) and BIM are coupled in the "LIO-BIM" framework for robust and accurate mobile robot localization and mapping relative to BIM models. LIO-BIM overlays the as-built map with BIM models, enabling automated progress monitoring. The framework is implemented and validated on the ConSLAM dataset, which includes periodically collected data from a 3D lidar, an inertial measurement unit, and a camera at a construction site. The validation tests show robust and accurate 3D localization and mapping relative to BIM models in real-time, enabling effective automated progress monitoring
Optimizing temperature, pressure, and waste heat utilization in PEM electrolyzers: a model-based approach to enhance integrated energy system efficiency
No full text“Green” hydrogen is regarded as a crucial energy carrier for defossilizing global energy systems. However, its production via electrolyzers remains energy-intensive and costly, hindering widespread adoption. Two promising strategies address these challenges: waste heat recovery from electrolysis and optimization of the interplay of electrolyzer operating parameters (i.e., current density, temperature, pressure). This paper combines these approaches to assess the potential energy and cost savings from optimized waste heat utilization and its influence on optimal operating parameter selection. To achieve this, a polymer electrolyte membrane electrolysis process model is integrated into a superordinate energy system model to investigate the interplay between waste heat recovery and parameter optimization. Two operational modes – prioritizing hydrogen production efficiency versus system-wide efficiency – are compared to a reference case without waste heat recovery. The results reveal that waste heat utilization achieves cost savings of 0.8 to 2.9% and energy savings of 0.6 to 1.9%, while parameter optimization yields cost savings of 5.9 to 6.4% and energy savings of 3.4 to 3.7%. The highest savings (7.7 to 9.2% cost, 4.6 to 5.3% energy) are achieved by combining these strategies through a holistically designed operational approach, balancing efficient hydrogen production with optimized heat provision. This requires accepting marginal electrolyzer efficiency losses to gain systemic benefits through improved heat integration by mainly increasing pressure and adapting load points (i.e., current density). Operational flexibility – enabling dynamic adjustments to align hydrogen production with intermittent PV availability and electricity prices – proves essential. To preserve this flexibility, electrolyzers should supplement rather than fully meet heat demand
A novel data concept for cutting processes through comprehensive experimental setup enabling grey-box models
No full textThis paper presents a novel experimental setup for recording the cutting process of external longitudinal turning in situ, capturing data such as cutting force, vibration, acoustic emission (AE), rake face temperature, surface quality, and tool wear. The primary objective of this setup is to generate reliable, repeatable data for developing a comprehensive grey-box model. To achieve this, the measurements were partly automated. Additionally, a complete post-processing pipeline has been introduced to combine all relevant data. To validate the setup, external longitudinal turning experiments were conducted and two of these were analysed for sensitivity to tool wear. This was achieved by analysing the mean and variance of the resultant force FZ, resultant vibrations aZ and AE signal, in order to demonstrate the impact of tool wear on the measurement. The experiments were also used to train an autoencoder to analyse the process data
Which Precipitation Dataset to Choose for Hydrological Studies of the Terrestrial Water Cycle?
No full textPrecipitation is a critical component of the terrestrial hydrological cycle. It plays a crucial role in shaping climate patterns and ecosystem dynamics. The quest for accurate measurements of global precipitation started over 150 years ago. Comprehensive evaluations of the estimates were rare until the 1970s, mainly due to the challenges of acquiring and maintaining up-to-date datasets. Nowadays, data availability is no longer an issue. However, in the face of the seemingly ever-growing number of available datasets, determining which one to rely upon poses a new obstacle, especially in the absence of global ground truth, further complicated by the interdependencies among datasets. Here, we identify the genealogy of multiple precipitation datasets and define multiplicity artifacts. Then, we compute an evaluation reference benchmark free of multiplicity artifacts to identify the dataset that best represents the artifact-free ensemble over different terrestrial spatial domains. These include countries, IPCC assessment report reference regions, major world river basins, land-cover types, elevation zones, biome categories, and Köppen–Geiger climate classes. It should be noted that the datasets assessed herein had a monthly temporal scale, and our findings might not apply to the study of climate extreme events regardless of the terrestrial spatial domain. We repeatedly found GPM IMERG Final v07 to emerge as the most representative dataset over multiple domains. Furthermore, we found that the dataset’s representativeness is largely influenced by how spatial domains are defined rather than their scale. SIGNIFICANCE STATEMENT: Over the past decades, we have amassed a vast array of precipitation datasets. While offering significant opportunities, this abundance of data creates a new challenge: which precipitation dataset should we use? In this work, we address this challenge by developing a method to identify and mitigate the impact of overlapping or dependent data sources, ensuring more reliable comparisons across diverse spatial domains. This is important because our results can help researchers select more reliable datasets and emphasize the need for a deeper understanding of the data used, thus avoiding misleading conclusions. Such efforts contribute to advancing hydrological sciences and the broader nexus fields, where reliable precipitation data serve as a cornerstone for modeling and projecting climate change impacts
Thermodynamics-informed graph neural networks for phase transition enthalpies
No full textPhase transition enthalpies, such as those for fusion, vaporization, and sublimation, are vital for understanding thermodynamic properties and aiding early-stage process design. However, measuring these properties is often time-consuming and costly, leading to increased interest in computational methods for fast and accurate predictions. Graph neural networks (GNNs), known for their ability to learn complex molecular representations, have emerged as state-of-the-art tools for predicting various thermophysical properties. Despite their success, GNNs do not inherently obey thermodynamic laws. In this study, we present a multitask GNN designed to predict vaporization, fusion, and sublimation enthalpies of organic compounds. We modified the loss function of the GNN, accounting for the thermodynamic cycle of the three phase transition enthalpies. To train the model, we digitized the extensive Chickos and Acree compendium, which encompasses 32,023 experimental measurements. Two approaches were explored: soft constraints, which guide the model toward thermodynamic consistency, and hard constraints, which enforce fully consistent predictions. The GNN achieved root mean squared errors (RMSEs) of 19.9 kJ/mol for sublimation, 11.0 kJ/mol for fusion, and 16.5 kJ/mol for vaporization enthalpies on the test set. Soft constraints were found to provide a good balance between accuracy and thermodynamic consistency, whereas hard constraints prioritized fidelity at the expense of predictive performance. When compared to the conventional Joback group contribution method the GNN demonstrated an improved accuracy and applicability range. This work underscores the potential of thermodynamics-informed GNNs for predicting thermodynamic properties accurately while maintaining consistency, paving the way for more reliable and efficient computational approaches