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Disentangled latent spaces for reduced order models using deterministic autoencoders
No full textData-driven reduced-order models based on autoencoders generally lack interpretability compared to classical methods such as the proper orthogonal decomposition. More interpretability can be gained by disentangling the latent variables and analyzing the resulting modes. For this purpose, probabilistic β-variational autoencoders (β-VAEs) are frequently used in computational fluid dynamics and other simulation sciences. Using a benchmark periodic flow dataset, we show that competitive results can be achieved using non-probabilistic autoencoder approaches that either promote orthogonality or penalize correlation between latent variables. Compared to probabilistic autoencoders, these approaches offer more robustness with respect to the choice of hyperparameters entering the loss function. We further demonstrate the ability of a non-probabilistic approach to identify a reduced number of active latent variables by introducing a correlation penalty, a function also known from the use of β-VAE. The investigated probabilistic and non-probabilistic autoencoder models are finally used for the dimensionality reduction of aircraft ditching loads, which serves as an industrial application in this work
Cross-sectoral reliability-constrained sizing of thermal storage in multi-energy systems
No full textThe increasing electrification in district heating systems through electric heat pumps and the resulting coupling between electrical and heating systems presents challenges to network operators and planners, but it also offers high flexibility potential in distribution network operation. The flexibility offered by electric heat pumps and thermal storages can play a vital role in providing affordable energy storage and the potential for load shifting. However, this flexibility comes with uncertainty as it depends on changing weather conditions and customer behavior. Therefore, the correct sizing of the thermal storage capacities in the planning phase of multi-energy systems (MES) is essential for guaranteeing sufficient flexibility for electrical network operation. Moreover, existing reliability metrics do not capture the interactions between the electrical and thermal domains of MESs. In this paper, a novel methodology is presented for optimal sizing under the uncertainty of thermal storage capacities in a heating network coupled to an electrical network. Distributionally robust chance-constrained optimization (DRCC) is used to model the system to limit the probability of insecure operation due to uncertainty in heat demand forecasting. The proposed approach is demonstrated on a modified MES and the results are compared to those obtained from a conventional deterministic optimization model. A new reliability metric, Expected Heat Not Supplied (EHNS), is introduced to evaluate system reliability. The proposed methodology is designed to provide network planners and operators with the optimal storage capacities needed to balance robustness against existing uncertainties, costs, and system reliability.Bundesministerium für Wirtschaft und Energie (BMWE
Sustainability Nexus AID: soil health
No full textAbstract The Sustainability Nexus Analytics, Informatics, and Data (AID) Programme of the United Nations University (UNU), aims to provide information, data, computational, and analytical tools to support the sustainable management and long-term security of natural resources using a nexus approach. This paper introduces the Soil Health Module of the Sustainability Nexus AID Programme. Healthy soil is crucial for life on Earth, and it is essential for ecosystem services and functioning, access to clean water, socioeconomic structure, biodiversity, and food security for the growing population of the world. Healthy soils contribute to mitigating the effects of climate change and reduce the consequences of extreme events such as flooding and drought. Healthy soils influence the hydrologic cycle by regulating transpiration, water infiltration, and soil water evaporation affecting land–atmosphere interactions. The Soil Health Module of the UNU Sustainability Nexus AID Programme aims to evolve into the ultimate focal point, supporting a diverse array of stakeholders with state-of-the-art data and tools that are essential for soil health monitoring and projection. This paper discusses the importance of adopting a nexus approach for ensuring soil health, explores the AID tools currently at our disposal for quantifying and predicting soil health, and concludes with recommendations for future effort and direction within the Sustainability Nexus AID Programme concerning soil health
Oxygen production via electrolysis: A model-based assessment of its impact on a climate-neutral German energy system
No full textThe integration of “green” hydrogen into the energy supply represents a key strategy for the defossilization of energy systems. However, its economic viability remains constrained by the currently high production costs. A possible strategy to enhance the economic feasibility of hydrogen-based energy systems is the system-integrated utilization of oxygen, a by-product of electrolysis. This study examines the potential of integrating electrolysis-derived oxygen into various industrial applications using an energy system optimization model of Germany. The analysis focuses on identifying the cost-saving potential, the resulting impact on the hydrogen and oxygen supply chains, and suitable industrial sites for oxygen utilization. The findings reveal that integrating electrolysis-derived oxygen into industrial processes offers substantial cost-saving opportunities while influencing the optimal configuration of hydrogen supply chains and infrastructure. Incorporating electrolysis-derived oxygen into existing industrial processes can reduce total system costs by up to 0.2% without significantly changing hydrogen infrastructure design or the average levelized costs of hydrogen. Further savings are achievable by introducing new applications such as oxy-fuel combustion and wastewater treatment. In this case, system costs can decrease by up to 1.3%, and average levelized costs of hydrogen can fall by 7%. These changes also shift optimal electrolyzer siting toward industrial locations with high oxygen demand. The high-value chemical industry, in particular, can cover nearly its entire newly created oxygen demand through electrolysis. The results highlight the systemic value of integrating electrolysis-derived oxygen and underscore the importance of including oxygen utilization in early hydrogen infrastructure planning
Relational SI/PI-database for a data-driven approach to PCB design automation and performance prediction
No full textThe introduction of machine learning (ML) methods into the design process of printed circuit boards drives the need for large quantities of readily available data. This paper addresses the problems of engineers to find ML-ready data that can be easily reused and combined to enhance printed circuit board (PCB) design by storing the defining parameters in a normalized format within a relational database It implements search and filter functions to obtain relevant data quickly. The database contains data that was used to address a variety of different signal integrity (SI) and power integrity (PI) related problems. Details of the database structure, necessary data conversion steps, currently stored datasets, and a statistical analysis there of are described. This database is capable to be automated to a degree that ML agents can interact with it
Rudder force calculation in the early design stage considering propeller–rudder interaction
No full textDue to the growing requirements on energy efficiency of ships, certain problems and challenges arise for the design of rudders and propellers. For the rudder, the focus changes for many ship types from solely being a manoeuvring device to positively influencing the propulsion. This paper summarizes a hybrid calculation method for the calculation of rudder forces and for the evaluation of the bidirectional interaction between propeller and rudder for the early design stage. The new hybrid calculation method couples a lifting line approach for multi-component-propulsors with a panel method and a two-dimensional boundary layer method. The calculation results of the developed method are validated with measurements from several model tests. Finally, an application for full-scale predictions is presented
Offline map updating and validation for autonomous driving using crowdsourced data
No full textAutonomous driving promises safer and more comfortable transportation with less traffic congestion than human driving. Autonomous driving can be achieved using landmark-based maps, which allow for precise localization and collision-free path planning. Therefore, it is essential to keep the maps updated and validated. Traditional approaches towards map updating and validation often fail to robustly keep pace with environmental changes, causing localization errors. Current research addresses the map updating and validation problem using either graph-based methods or feature-based methods online, i.e. running while the vehicles are traversing the environment, which is computationally demanding and unscalable. In this paper, an offline map updating and validation framework is presented using crowdsourced data, which is abundantly available and ubiquitous. To integrate multiple observations and improve map accuracy and reliability, the framework couples data fusion techniques, including the density-based spatial clustering of applications with noise (DB-SCAN) algorithm, the K-D tree data structure, and Dempster-Shafer theory. The framework is validated through multiple test scenarios, including adding new landmarks and removing deleted ones. As a result, the map updating and validation framework effectively integrates crowdsourced data, enhancing the accuracy and reliability of map updating and validation. The findings highlight the potential of crowdsourced data to improve map validation processes in autonomous driving
Preparation and characterization of copper-crosslinked alginate–hyaluronic acid aerogels as potential wound dressing materials with enhanced antibacterial properties
No full textThe development of advanced wound dressing materials with enhanced antibacterial properties is critical for improving patient outcomes and reducing infection risks. This study introduces a novel bio-based aerogel composed of copper-crosslinked alginate and hyaluronic acid, synthesized using supercritical gel drying techniques. Alginate and hyaluronic acid polymers are widely used in the pharmaceutical and medical industries because of their nontoxicity, biodegradability, and biocompatibility. This study aimed to create an aerogel that could be used as a potential wound dressing material by crosslinking hyaluronic acid and alginate with copper. The bio-based aerogel was prepared by ionic gelation and supercritical gel drying. The prepared materials were characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), BET surface area analysis, and energy-dispersive X-ray fluorescence (XRF). Moreover, the aerogel wound dressing properties were evaluated in terms of fluid uptake and antibacterial activity against S. aureus and E. coli. The physicochemical characterization of the prepared aerogels revealed their unique structural and morphological features, which are influenced by copper ion concentration and crosslinking time. Regarding their wound dressing evaluation, both aerogel and hydrogel were found to have antibacterial properties when tested on S. aureus with inhibition zones of (36 mm, 23 mm) and E. coli (31.6 mm, 21 mm) for hydrogel and aerogel, respectively. Also, excellent fluid uptake was found to reach up to 743%. These findings underscore the potential of copper-crosslinked alginate–hyaluronic acid aerogels as innovative wound dressing materials that combine superior antibacterial efficacy with excellent fluid management, paving the way for improved wound healing solutions
Simulating situational overview in large-scale UAS networks using demand driven mobility
No full textRecently, Rate Decay Flooding (RDF) has been proposed as a special-purpose protocol to facilitate network-wide information exchange in large-scale urban Unmanned Aircraft System (UAS) networks. Nevertheless, mobility is a crucial aspect determining the performance of these protocols. In this work, we use randomly generated city geometries and flight demands to simulate Demand Driven Mobility (DDM) that represents plausible trajectories for a package delivery scenario in urban areas. These trajectories are then fed into a network simulator to obtain realistic performance measurements of the proposed protocols. Furthermore, we compare the results to Random Direction Mobility (RDM) widely used in UAS communication research. We quantify the performance with the dissemination rate, which is a measure of how effectively information is distributed within the network. Our results show, that even if matched for area and number of UAS, the choice of mobility has a strong influence on the performance. For low density, DDM causes temporary UAS clusters to form around drone ports that are not connected to UAS on delivery flights to the outskirts of the city, which results in a more poorly connected network compared to RDM. In high-density scenarios, where traffic is highly concentrated around drone ports, UAS located between the drone ports have to carry high amounts of data between them
Wear due to fatigue initiation
No full textPersson and coauthors have recently proposed an extension of the Rabinowicz idea for fatigue wear at different scales of roughness, where Paris’ crack growth law is applied to ”potential” wear particles. However, Persson's theory suffers from the fact that initial size of defects is unknown and fatigue life is not entirely due to propagation, so we investigate a different formulation, where a law for initiation of cracks is used for a specimen with initial roughness of engineering interest. We find that results (in particular dependence on amplitude of roughness, and on friction coefficient) are qualitatively similar to the original Persson and coworkers’ theory, but may differ substantially quantitatively. As the assumption of a constant fatigue threshold may be incorrect for short cracks, both fatigue limit and fatigue threshold are made dependent of crack size, using the Murakami formulation as one of the possible alternatives. This makes wear rate be sensitive to the fine scale details of the roughness spectrum, which has an effect on increasing wear rate and small particles emission. The model seems to have qualitative trends in agreement with experiments, but obviously wear is a very complex phenomenon and many factors may be not captured