20005 research outputs found
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Manufacturing-as-a-Service (MaaS) to Increase value chain resilience and circularity: towards a systematic methodology for manufacturing process servitization and value chain orchestration
No full textIn the current dynamic, volatile, and uncertain world, manufacturing companies face increasing pressure to respond efficiently and rapidly to disruptions, while at the same time adopting sustainable and circular practices. This paper focuses on Manufacturing-as-a-Service (MaaS) for enhancing resilience and promoting circularity in manufacturing value chains. Based on the limitations of existing MaaS research and scarcity of implementations, a systematic methodology is proposed for manufacturing process servitization, manufacturing process and product matching, and manufacturing process connection, collaboration and execution. This methodology supports the scaling of MaaS to a large variety of manufacturing processes, enabling dynamic distributed networks of manufacturing resources. Resiliency enhancing processes are outlined using MaaS for creating alternative value chain orchestrations, as responses to disruptions. Moreover, processes promoting circularity in relation to MaaS and resilience are outlined. This act as support for finding appropriate value chains partners to enable a circular product system and in increasing resilience and circularity simultaneously by reusing and remanufacturing critical components
Hydrogen Supply Networks‘ Evolution for Air Transport – Final Project Report
No full textThis report provides a comprehensive assessment of the potential for a liquid hydrogen (LH2 ) supply infrastructure for hydrogen (H2 )-powered aviation in Europe. H2 is a key defossilization tool and will be critical for emission reduction in aviation, either through direct use as a fuel or as a feedstock for sustainable aviation fuels. Rather than advocating for H2-powered aviation as the main solution, this report explores how such an LH2 supply infrastructure could be realized, what implications it would entail, and how it might interact with the broader European energy system. To capture the inherent uncertainties in technology development and policy commitment, we developed a series of scenarios for the uptake of H2 –powered aircraft: the Baseline, Ambitious Policy, and Moonshot scenario. Using a system dynamics model, we determined the future LH2 demands for a European airport network for these scenarios. In order to meet these demands as cost-effectively as possible, we developed a H2 supply network model which determines the cost optimal supply network for a target picture in the year 2050 as well as the transition path to reach that target picture. The supply network can consist of multiple H2 supply routes available for the airports (on-site LH2 production, GH2 pipelines, LH2 trucks and LH2 vessels). The potential H2 supply volumes and costs are determined using an energy system transition model which is used to evaluate the integration of H2 production into the general energy system transformation on three perspectives: International H2 import options, H2 production within the European energy system as well as the impact of LH2 supply for aviation on the local energy system. As LH2 availability and supply costs vary greatly between airports, we use a flight network model to evaluate how these costs influence the future flight networks. The study also includes the ecological evaluation using a life cycle assessment to determine the ecological impact of H2 production and compare the different supply routes. In addition, we examine potential business models, stakeholder perspectives, and policy instruments to support this transition, with the goal of informing decision-making in industry, policy, and science. Finally, the macroeconomic impact of implementing such a supply infrastructure is analysed using a Social Accounting Matrix (SAM)-based multiplier model.Bundesministerium für Forschung, Technologie und Raumfahrt (BMFTR
Decentralized sensor fault diagnosis for wireless structural health monitoring systems using Artificial Intelligence of Things
No full textStructural health monitoring (SHM) is a non-destructive evaluation technique that utilizes sensor data for assessing the condition of civil infrastructure. Sensors in SHM systems may experience faults, which may influence the accuracy, reliability, and performance of SHM systems. The timely detection, isolation, and accommodation of sensor faults in SHM systems has been the focus of sensor fault diagnosis (FD) approaches, which have increasingly been employing artificial intelligence (AI) algorithms due to the effectiveness of AI in sensor FD. However, current AI-based FD approaches require transmitting large amounts of raw sensor data to centralized servers for offline analysis, resulting in inefficiencies as well as computational burdens on centralized servers. This paper introduces a decentralized sensor fault diagnosis (DSFD) approach for wireless SHM systems using Artificial Intelligence of Things (AIoT). In particular, AI-based FD models are embedded into wireless sensor nodes of SHM systems to detect, isolate, and accommodate sensor faults. By embedding the FD models into the wireless sensor nodes, only high-level information, specifically the status of the sensors, is transmitted to centralized servers. As a result, data transmission inefficiencies as well as computational burdens on centralized servers are reduced. The proposed DSFD approach is validated in a controlled laboratory experiment, in which custom- built wireless senor nodes are installed on a test structure that is dynamically excited using a shake table. After training and embedding the AI-based FD models into the custom-built wireless sensor nodes, sensor faults are artificially injected into the sensor data, demonstrating the ability of the DSFD approach to diagnose sensor faults in a decentralized manner. The results of the validation test corroborate the capability of the proposed approach to efficiently ensure the accuracy, reliability, and performance of SHM systems
Modelling Aquifer Thermal Energy Storage (ATES) system with buoyancy flow
No full textAquifer Thermal Energy Storages (ATES) as long-termstorages have a strong potential to address the seasonaldiscrepancy of supply and demand of thermal energy. Theoperation of high-temperature ATES (HT-ATES) and theirintegration into district heating systems are the subjectof current research projects. Buoyancy plays an importantrole in determining how HT-ATES performs. The target ofthis paper is to present a system model in Modelica thattakes these buoyancy effects into account. The validationwith experimental data and numerical simulations shows thatthe system model represents the buoyancy effects well. Asensitivity analysis underlines the importance ofoptimizing the grid structure and shows that a highresolution in the aquifer is necessary, especially in thevertical direction. Finally, a 10-year simulation shows thedeviation of the heat recovery factor, i.e. the ratio of theamount of heat extracted to the amount of heat injected,between a model with and without buoyancy effects
An AI-powered auto-completion tool for Solidity smart contracts
No full textSolidity smart contracts are widely used to implement decentralized applications. However, their development remains challenging due to the language’s domain-specific complexity, the immutability of deployed contracts, which prevents post-deployment fixes, and the high risk of introducing security-critical vulnerabilities. While Large Language Models (LLMs) have advanced code generation across general domains, they often struggle to meet the structural and security-specific demands of smart contract development. Therefore, this paper presents a domain-adapted code completion model trained on 22,000 labeled code constructs extracted from Solidity contracts. The model is built on a transformer-based architecture and fine-tuned using Quantized Low-Rank Adaptation (QLoRA), a parameter-efficient method. The dataset is processed to highlight secure coding patterns and structural semantics, enabling the model to learn from both preceding and succeeding contexts. Evaluation using perplexity, the Bilingual Evaluation Understudy (BLEU) score, and the Metric for Evaluation of Translation with Explicit Ordering (METEOR) shows significant improvements with consistent gains across all three metrics compared to the base model. These results demonstrate that targeted adaptation of language models can significantly enhance coding support in Solidity smart contracts
Direktumschläge zwischen Schiffen und Lkw: Überspringen der Lagerung von Containern auf Seehafenterminals
No full textContainer yards are increasingly becoming bottlenecks at the terminals. To address this, new approaches are needed. One way to redesign processes at the terminal is the direct handling of containers on the seaside. This study employs a discrete-event simulation model to analyse the effects of delayed truck arrivals on quay crane productivity during direct handling between vessels and trucks. In this context, direct handling of containers refers to the loading and unloading of containers between vessels and trucks without intermediate storage in the container yard. A simulation model using Tecnomatix Plant Simulation replicates a terminal employing both conventional and direct handling, examining various truck delay scenarios. Results indicate that minor truck delays mildly affect quay crane productivity, whereas significant delays considerably diminish productivity, especially when a larger share of containers is handled directly. Although direct handling offers efficiency potential, delayed trucks pose significant planning challenges. Future research will aim to develop strategies to mitigate these impacts, such as flexible export container loading sequences
Datasets for structural and mechanical properties of nanoporous networks from FIB reconstruction
No full textThis dataset paper presents a comprehensive archive of 3D tomographic reconstruction image files, volume mesh files for finite element simulations, and tabulated structural and mechanical properties data of nanoporous gold structures. The base material is nanoporous gold, fabricated using a dealloying process, with a solid fraction of approximately 0.30. The NPG samples with ligament sizes ranging from 20 nm to 400 nm were prepared by dealloying and by controlling the thermal annealing process. The original data consist of tomographic TIFF files acquired through Focused Ion Beam/Scanning Electron Microscopy (FIB/SEM) 3D reconstruction, as detailed in Philosophical Magazine 2016 96 (32-34), 3322-3335. At each ligament size, six sets of 3D tomographic images were obtained from different regions of the same sample to ensure representative data. New simulations and analyses were conducted based on the 3D image data. The resulting structural and mechanical property data of nanoporous gold are reported for the first time in this dataset paper. Volume meshing of the 3D reconstructed data was performed using Simpleware software. Structural parameters, including surface area, solid volume, and solid volume fraction of the nanoporous network, were extracted from the meshed volumes. Structural connectivity was assessed from the 3D microstructures. The meshed volumes were then used as input for finite element simulations performed in Abaqus to evaluate mechanical responses under uniaxial compression along all three principal axes respectively. From the resulting stress–strain curves, the Young’s modulus and yield strength of each structure were determined. Both elastic and plastic Poisson’s ratios were analyzed from true strain increments. This dataset includes the 3D tomographic images, corresponding volume mesh files, mechanical behavior data and tables summarizing the structural and mechanical properties. The archived data serve as a database for nanoporous network materials and can be reused for numerical simulations, additive manufacturing, and machine learning applications within the materials science community. All files are openly accessible via the TORE repository at https://doi.org/10.15480/882.15230.Deutsche Forschungsgemeinschaft (DFG
Biomechanical evaluation of shape-optimized CAD/CAM magnesium plates for mandibular reconstruction
No full textMagnesium CAD/CAM miniplates are a promising alternative to titanium plates for mandibular reconstruction. However, gas formation is an inherent part of the magnesium degradation process, and thus, the quantity of magnesium used in fixation scenarios should be limited. Previous studies described several strategies to limit material volume, such as plate thickness reduction and shape-optimization. In particular, shape-optimization has been described as a strategy to limit material volume while maintaining mechanical integrity. In consequence, the present study compared a shape-optimized CAD/CAM magnesium miniplate with standard CAD/CAM magnesium miniplates of varying thicknesses using a biomechanical finite element model. A single-segment mandibular reconstruction was chosen as the investigative scenario, evaluated under different biting tasks to assess the different plate shapes. The shape-optimized magnesium plate demonstrated similar primary fixation stability compared to standard CAD/CAM magnesium miniplates, despite having reduced plate material and surface area. Shape optimization could help minimize magnesium volume and surface area to mitigate the issue of gas formation during the degradation process in vivo while maintaining biomechanical performance comparable to common CAD/CAM miniplates
Diurnal variability of global precipitation: insights from hourly satellite and reanalysis datasets
No full textAccurate estimation of precipitation at the global scale is of utmost importance. Even though satellite and reanalysis products are capable of providing high-spatiotemporal-resolution estimations at the global level, they are associated with significant uncertainties that vary with regional characteristics and scales. The uncertainties among precipitation estimates, in general, are much higher at the sub-daily scale compared to daily, monthly, and annual scales. Therefore, evaluating these sub-daily estimations is of specific importance. In this context, this study explores the diurnal cycle of precipitation using all the currently available space-borne and reanalysis-based precipitation products with at least hourly resolution at the quasi-global scale (60° N-60° S), i.e. Integrated Multi-satellitE Retrievals for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Center Morphing (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and ECMWF Reanalysis v5 (ERA5). The diurnal variability of precipitation is estimated using three parameters, namely, precipitation amount, frequency, and intensity, all remapped at a common resolution of 0.25° and 1 h. All the estimates represent the spatiotemporal variation across the globe well. Nevertheless, considerable uncertainties exist in the estimates regarding the peak precipitation hour, as well as the diurnal mean precipitation amount, frequency, and intensity. In terms of diurnal mean precipitation, PERSIANN shows the lowest estimates compared to the other datasets, with the largest difference observed over the ocean rather than over land. As for diurnal frequency, ERA5 exhibits the highest disparity among the estimates, with a frequency twice as high as that of the other estimates. Furthermore, ERA5 shows an early diurnal peak and highest variability compared to the other datasets. Among the satellite estimates, IMERG, GSMaP, and CMORPH exhibit a similar pattern, with a late-afternoon peak over land and an early-morning peak over the ocean. Overall, it emphasizes the need to integrate diverse datasets and exercise caution when relying solely on individual precipitation products to ensure a thorough understanding and precise analysis of global precipitation patterns