335598 research outputs found
Sort by
Cities as laboratories for the energy transition
No full textCities are at the forefront of the energy transition, playing a pivotal role in shaping the way we generate, consume, and manage energy. At the urban level, frictions in the energy regime and specific local constellations can open space for experimentation with energy system innovations with the potential to transform the energy regime beyond the local context. This paper proposes that urban stakeholders not only adopt novel strategies and technologies, positioning cities as testing grounds and pivotal sites for implementing change but also innovate and explore new methods to improve energy efficiency, alter energy consumption patterns, and innovate energy production. Focusing on the local scope of action and the urban level as a level for experimentation, this paper applies the multilevel perspective Geels (Res Policy 31:1257–74, 2002), which analyzes transformations as an interplay of three different levels: landscape, regime, and niches. The results are based on case studies in two metropolitan areas in Germany, Frankfurt/Main and Berlin. Starting with a literature review on the role and scope of actions for cities, it presents and discusses findings from qualitative guideline-based actor and expert interviews, providing insight into the assessment, experiences and perceptions of key actors of the local energy systems of these two urban areas. The results highlight the scope and fields of action of cities and highlight the high potential for local experimentation as well as existing challenges and barriers
Simulating Incompressible Fluid Flows with Uncertainty Using Lattice Boltzmann Methods
No full textUncertainty quantification (UQ) has become a critical component in computational fluid dynamics (CFD), particularly for assessing the reliability of simulation results in the presence of uncertain parameters such as inlet velocity, viscosity, or boundary conditions. The lattice Boltzmann method (LBM), a mesoscopic CFD technique known for its parallel scalability and flexibility with complex geometries, provides a promising platform for integrating UQ. However, systematic UQ capabilities remain underdeveloped in current LBM-based frameworks.
This dissertation develops a unified UQ framework for incompressible LBM simulations, combining both non-intrusive and intrusive approaches. The non-intrusive strategy is implemented by extending the open-source OpenLB library with a modular UQ module that supports Monte Carlo sampling, quasi-Monte Carlo methods, and stochastic collocation (SC) based on generalized polynomial chaos (gPC). The OpenLB-UQ module automates sampling, parallel execution, and statistical post-processing, enabling scalable UQ workflows. Validation on canonical benchmarks—such as the Taylor–Green vortex and flow past a cylinder—demonstrates accurate moment estimation and parallel performance gains.
In parallel, the first fully coupled stochastic Galerkin (SG) LBM is proposed, which reformulates the LBM equations using polynomial chaos expansions.
The proposed intrusive SG LBM directly evolves the coefficients of the discrete-velocity distribution functions obtained via polynomial chaos expansion. Its algorithmic structure fully preserves the LBM streaming and collision processes, ensuring structural consistency with standard LBM frameworks.
The SG LBM achieves spectral convergence and reduces computational cost by a factor of five to six compared to Monte Carlo methods in representative cases.
To demonstrate real-world applicability, we applied an uncertain data assimilation workflow based on OpenLB-UQ to an urban wind simulation around an isolated building in Reutlingen (Germany).
Measurement uncertainty was directly injected into the inflow boundary data and propagated through a non-intrusive SC LBM pipeline, yielding spatio-temporal statistics of the velocity field across the domain.
The workflow enabled the computation of mean and standard deviation fields, the identification of flow-sensitive zones such as wakes and shear layers, and the derivation of confidence intervals at monitoring probes. This provided interpretable uncertainty maps that respect the statistical nature of measurement-driven inflow and highlighted their relevance for urban planning and wind engineering applications.
Together, these contributions provide a scalable and comprehensive, open-source UQ toolkit for LBM-based CFD. Conclusively, this work advances efficient uncertainty-aware simulations of incompressible flows across scientific and engineering domains
An Evaluation of Large Language Models for Procedural Action Anticipation
No full textThis study evaluates large language models (LLMs) for their effectiveness in
long-term action anticipation. Traditional approaches primarily depend on
representation learning from extensive video data to understand human activities, a process fraught with challenges due to the intricate nature and variability of these activities. A significant limitation of this method is the difficulty in obtaining effective video representations. Moreover, relying solely on video-based learning can restrict a model’s ability to generalize in scenarios involving long-tail classes and out-of-distribution examples. In contrast, the zero-shot or few-shot capabilities of LLMs like ChatGPT offer a novel approach to tackle the complexity of long-term activity understanding without extensive training. We propose three prompting strategies: a plain prompt, a chain-of-thought-based prompt, and an in-context learning prompt. Our experiments on the procedural Breakfast dataset indicate that LLMs can deliver promising results without specific fine-tuning
Ion flow measurements in the Wendelstein 7-X stellarator towards a validation of neoclassical theory
No full textWe present a systematic experimental study of the ion plasma velocity field in the Wendelstein 7-X (W7-X) stellarator, in multiple magnetic configurations and operation conditions, and assess the ability of neoclassical theory to explain the observations. In this assessment, we employ a forward model of the velocity measurement from the charge exchange recombination spectroscopy (CXRS) diagnostic with a rigorous treatment of the ion flow and line of sight geometries, as well as wavelength corrections due to atomic physics and instrumental effects.The minimisation of the absolute difference between the experimentally measured velocity profiles and those predicted by the forward model enables the inversion of radial profiles of the radial electric field and the net parallel impurity ion velocity, which can be directly compared with neoclassical computations. Non-trivial redundancy in the multiple simultaneous velocity measurements allows to self-calibrate instrumental wavelength drifts within the forward model. A recent extension in the model representation has resulted in a substantial quantitative improvement in the agreement between CXRS velocity measurements and neoclassical flow expectations, which are now shown to be compatible in the majority of the cases. Furthermore, we show that the flow inversions are statistically robust and consistent when two different emission lines are used. The radial electric field compares well with independent Doppler reflectometry measurements within the radial region where they overlap. By and large, the observations presented support the validity of neoclassical flow calculations in the plasma core and mid-radius region (ρ0.7) in W7-X. However, with the presently accessible experimental accuracy and magnetic configuration variations, a clear configuration dependence of the ion flow could not be determined
Leveraging Data Shapes in Large Language Model Contexts for Question Answering on Public and Private Knowledge Graphs
No full textCircuit Model for Hysteresis Losses in Twisted Stacked HTS Cables
No full textThe twisted stacked tape cable (TSTC) configuration, which employs high-temperature superconducting (HTS) tapes, is among the most promising conductor designs for fusion applications, particularly in Tokamak reactors. This study proposes an alternative approach to finite element method (FEM) models to analyze such a realistic TSTC geometry while significantly reducing the computational cost of conventional 3D finite element method (FEM) modeling. The analysis is performed using CALYPSO, a non-linear 3D circuit model originally developed for the study of no-insulation HTS (NI-HTS) coils. In this work, CALYPSO is applied to investigate the electromagnetic behavior of a twisted stack of superconducting tapes over one twist pitch. In this configuration, the stack of HTS tapes is twisted around the cable axis, rather than its own. The model provides a detailed representation of the stack geometry, capturing both the current distribution within individual tapes and the coupling currents between them. The results obtained from CALYPSO, in terms of instantaneous power losses, are benchmarked with numerical simulations based on alternative modeling approaches
Deer as sentinels of emerging environmental pollution: Assessing contamination patterns and seasonal variations
No full textEnvironmental pollution caused by various chemicals is a pressing issue that affects every ecosystem. National parks play a special role in protecting and preserving nature. However, despite their special status as protected areas, they are not immune to environmental pollution. We assessed the pollution burden of ungulate populations in eight national parks from all over Germany. We analyzed 118 compounds from eight pollutant groups (indicators for anthropogenic pollution, active pharmaceutical ingredients (APIs), polycyclic aromatic hydrocarbons (PAHs), personal care product ingredients (PCPs), pesticides, plasticizers, persistent organic pollutants (POPs), and industrial chemicals) in a total of 442 liver samples from Cervus elaphus and Dama dama taken between October 2023 and January 2025. Results revealed the presence of 61 different analytes in the samples. Parallel detected analytes in samples ranged from 14 to 33, (median 18). APIs and pesticides went largely undetected, whilst contamination with PAHs and PCPs was comparable among all eight parks and three age groups (fawn, subadult and adult). We are the first to report on such a wide range of specific compounds in ungulates, particularly emerging groups of pollutants. Determined concentrations of anthropogenic pollutants, industrial chemicals, plasticizers and POPs differed between parks. Statistical analyses and regression model fitting provided no evidence of seasonal variation in the contamination with anthropogenic pollutants, industrial chemicals, PAHs and POPs. The high number of analytes detected in parallel is alarming in terms of potential cocktail effects and underlines the importance of sustainable management of environmental pollution and further research into emerging pollutants
Real-world energy data of 200 feeders from low-voltage grids with metadata in Germany over two years
No full textThe last mile of the distribution grid is crucial for a successful energy transition, as more low-carbon technology like photovoltaic systems, heat pumps, and electric vehicle chargers connect to the low-voltage grid. Despite considerable challenges in operation and planning, researchers often lack access to suitable low-voltage grid data. To address this, we present the FeederBW dataset with data recorded by the German distribution system operator Netze BW. It offers real-world energy data from 200 low-voltage feeders over two years (2023-2025) with weather information and detailed metadata, including changes in low-carbon technology installations. The dataset includes feeder-specific details such as the number of housing units, installed power of low-carbon technology, and aggregated industrial energy data. Furthermore, high photovoltaic feed-in and one-minute temporal resolution makes the dataset unique. FeederBW supports various applications, including machine learning for load forecasting, conducting non-intrusive load monitoring, generating synthetic data, and analyzing the interplay between weather, feeder measurements, and metadata. The dataset reveals insightful patterns and clearly reflects the growing impact of low-carbon technology on low-voltage grids
Combined Foaming and Surface Structuring of Polymers With Supercritical Carbon Dioxide ( scCO) Applying an Incomplete Saturation Strategy
No full textSupercritical carbon dioxide (scCO) is a widely applied solvent utilized in many physical–chemical processes, including the foaming or structuring of polymers. Here, we demonstrate that the last two can be combined in one process step applying an incomplete saturation strategy, resulting in foamed and structured polymer sheets with multi-functionality. Caused by the nano- and micropores, the polymer sheets scatter light, leading to a white color impression. Due to the imprinted nano- and microstructures, the polymer surfaces feature an increased water contact angle and reduced optical reflection due to the moth-eye effect
Peptide Arrays as Tools for Unraveling Tumor Microenvironments and Drug Discovery in Oncology
No full textPeptide arrays represent a powerful tool for investigating a wide application field for biomedical questions. This review summarizes recent applications of peptide chips in oncology, with a focus on tumor microenvironment, metastasis, and drug mechanism of action for various cancer types. These high-throughput platforms enable the simultaneous screening of thousands of peptides. We report on recent achievements in peptide array technology for tumor microenvironments, an enhanced ability to decipher complex cancer-related signaling pathways, and characterization of cell-adhesion-mediating peptides. Furthermore, we highlight the applications in high-throughput drug screenings for development of immune therapies, e.g., the development of novel neoantigen therapies of glioblastoma. Moreover, epigenetic profiling using peptide arrays has uncovered new therapeutic targets across various cancer types with clinical impact. In conclusion, we discuss artificial intelligence-driven peptide array analysis as a tool to determine tumor origin and metastatic state, potentially transforming diagnostic approaches. These innovations promise to accelerate the development of precision cancer approaches