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Beschleunigtes Materialdesign durch künstliche Intelligenz im Forschungsdatenmanagement
No full textAm Beispiel von Polyurethanschaumstrukturen entwickelt diese Arbeit einen modularen, FAIRen Workflow für datengetriebene Materialentwicklung. Über KI-basierte Segmentierung, generative 3D-Modelle und Simulationen werden mikrostrukturelle Eigenschaften automatisiert analysiert und mechanische Kennwerte zuverlässig vorhergesagt. Die generischen Workflows und eine transparente Datenverwaltung beschleunigen den Entwicklungsprozess und lassen sich flexibel auf andere Materialien übertragen
Zwischenbericht für den Berichtszeitraum 01.01.2025-30.06.2026 zum BMFTR-Förderkonzept FORKA für das Projekt "Entwicklung eines Beprobungssystems inklusive qualitätsgesichertem Beprobungsverfahren für nicht zugängliche Bereiche (Bero)"
No full textOn the consistency of pseudo-potential lattice Boltzmann methods
No full textWe derive the partial differential equation (PDE) to which the pseudo-potential lattice Boltzmann method (P-LBM) converges under diffusive scaling, providing a rigorous basis for its consistency analysis. By establishing a direct link between the method\u27s parameters and physical properties—such as phase densities, interface thickness, and surface tension—we develop a framework that enables users to specify fluid properties directly in SI units, eliminating the need for empirical parameter tuning. This allows the simulation of problems with predefined physical properties, ensuring a direct and physically meaningful parametrization. The proposed approach is implemented in OpenLB, featuring a dedicated unit converter for multiphase problems. To validate the method, we perform benchmark tests—including planar interface, static droplet, Galilean invariance, and two-phase flow between parallel plates—using R134a as the working fluid, with all properties specified in physical units. The results demonstrate that the method achieves second-order convergence to the identified PDE, confirming its numerical consistency. These findings highlight the robustness and practicality of the P-LBM, paving the way for accurate and user-friendly simulations of complex multiphase systems with well-defined physical properties
A Search for Millimeter-bright Blazars as Astrophysical Neutrino Sources
No full textThe powerful jets of blazars have been historically considered as likely sites of high-energy cosmic-ray acceleration. However, the particulars of the launched jet and the locations of leptonic and hadronic jet loading remain unclear. In the case when leptonic and hadronic particle injection occur jointly, a temporal correlation between synchrotron radiation and neutrino production is expected. We use a first catalog of millimeter wavelength (95–225 GHz) blazar light curves from the Atacama Cosmology Telescope for a time-dependent correlation with 12 yr of muon neutrino events from the IceCube South Pole Neutrino Observatory. Such millimeter emission traces activity of the bright jet base, which is often self-absorbed at lower frequencies and potentially gamma-ray opaque. We perform an analysis of the population, as well as analyses of individual, selected sources. We do not observe a significant signal from the stacked population. TXS 0506+056 is found as the most significant, individual source, though this detection is not globally significant in our analysis of selected active galactic nuclei. Our results suggest that the majority of millimeter-bright blazars are neutrino dim. In general, it is possible that many blazars have lighter, leptonic jets, or that only selected blazars provide exceptional conditions for neutrino production
Clustering Analysis to Determine the Optimizing Potentials in Drivetrain Consumption with SHAP Analysis
No full textWith the increasing demand for sustainable transportation in the face of challenges such as climate change and urbanization, optimizing the energy efficiency of Electric City Buses (ECBs) is essential. This study employs explainable artificial intelligence techniques, specifically SHapley additive expansion (SHAP), to assess the influence of factors such as vehicle speed, acceleration, and braking on the energy consumption of the drivetrain. The data is segmented into distinct scenarios, including acceleration, starting, curves, uphill, and downhill driving. In driving conditions like curves or uphill and downhill routes, the brake pedal position, alongside the accelerator position and vehicle speed, emerged as key factors impacting drivetrain consumption. Secondly, the study delves into analyzing driving behavior during bus stop entries and leaving instances, employing methods like Deep Autoencoder-based Clustering (DAC) and Self-Organizing Map (SOM). This analysis identified groups with energy-efficient and energy-inefficient driving behaviors, with certain clusters showing high acceleration and low braking use, particularly during nighttime or low-traffic conditions. These insights highlight the potential for energy savings by promoting smoother, more consistent driving styles, especially as electric buses approach or depart from bus stops
NaWuReT: The Early‐Career Network for Chemical Reaction Engineering
No full textNaWuReT (dt. “Nachwuchsreaktionstechnik”) is the early-career network of the DECHEMA Section on Chemical Reaction Engineering. Since its founding in 2011, it has connected early-career reaction engineers from academia and industry, created spaces for exchange across generations, and supported professional development through recurring formats such as the NaWuReT Colloquium, summer schools, and the NaWuReT-Grant for international research stays. This article summarizes NaWuReT\u27s origins, its role within DECHEMA, and the activities through which the network strengthens community building, international collaboration, and education in chemical reaction engineering
Design principles and AC loss mitigation strategies for a 15 MVA HTS transformer
No full textOffshore wind generators are expected to exceed 15 MVA in the coming years, necessitating compact and light-
weight step-up transformers. While high-temperature superconducting (HTS) transformers offer high efficiency
and compact size, their complex AC loss behavior in large-scale devices is still not sufficiently understood. In
this work, we present a comprehensive investigation on practical strategies for reducing AC loss in a 15 MVA
HTS transformer based on the primary industrial criteria of efficiency, weight, and cost. We employed an
efficient numerical method to model up to thousands of HTS tapes in low-voltage (LV) and high-voltage (HV)
windings in detail. Using this framework, we evaluated the influence of key design and operating parameters
on the AC loss, including winding height, axial gap between the cables, radial distance between HV and LV
windings, winding height difference, number of parallel conductors, voltage per turn, magnetic flux diverters,
and temperature. These analyses enable the development of four improved transformer designs at operating
temperatures of 20 K, 70 K, and two configurations at 77 K that balance AC loss reduction with conductor
cost and core weight. The results provide validated design principles and practical guidelines for developing
next-generation HTS transformers that are compact, light-weight, cost-effective, and suitable for large-scale
offshore wind energy systems
Reconstruction of a Freeway Control Systems’ Algorithm based on Convolution Neural Networks
No full textFreeway Control Systems (FCS) play a vital role in enhancing road safety and traffic efficiency by dynamically managing traffic through variable speed limits, overtaking restrictions, and warning messages. An accurate representation of FCS behavior in traffic simulations is essential for realistic modeling. However, the manual implementation of FCS logic in simulations is time-consuming and requires high customization. This study proposes a data-driven approach to automatically reconstruct FCS control algorithms from historical traffic and display data. The models achieved prediction accuracies of at least 87%, effectively capturing key behaviors such as congestion-related speed reductions. Among the architectures evaluated, the baseline Convolutional neural network offered the best balance of performance and computational efficiency. At the same time, more complex models showed promise for further accuracy gains with continued development. These findings demonstrate the feasibility and potential benefits of integrating FCS models based on neural networks into traffic simulations
Geopolitik der Energietransformation: Europa im Spannungsfeld von Klimaschutz und Energiesicherheit (Prof. Dr. Rainer Quitzow)
No full textDer Aufbau neuer Energieinfrastrukturen und Industrien bringt neue Formen staatlicher Eingriffe in die Wirtschaft und veränderte globale Wettbewerbsbedingungen mit sich. Prof. Dr. Rainer Quitzow (Leiter der Forschungsgruppe "Geopolitik der Energie- und Industrietransformation" am Forschungsinstitut für Nachhaltigkeit, GFZ Helmholtz-Zentrum für Geoforschungbeleuchtet) skizziert in seinem Vortrag diese Veränderungen und stellt die Frage, wie die EU vor diesem Hintergrund klimapolitische Ambition, wirtschaftliche Wettbewerbsfähigkeit und geopolitischen Einfluss erhalten kann.
Der Vortrag fand am 22. Januar 2026 am Karlsruher Institut für Technologie (KIT) statt. Er ist Teil der Vortragsreihe Colloquium Fundamentale mit dem Titel „Watt jetzt? Energiewende zwischen Technologie und Teilhabe“.
Weitere Informationen: https://www.forum.kit.edu/19559.ph
Analysis of CH2O ⋅ OH as marker for the heat release rate from air to pure oxy-fuel flames at elevated preheat temperature, pressure and strain
No full textThis work presents a numerical investigation of the applicability of the product of formaldehyde (CH2O) and hy
droxyl radicals (OH), denoted as CH2O ⋅ OH, as a marker for the local heat release rate (HRR) in CH4 flames over
a broad range of boundary conditions. Both laminar freely propagating one-dimensional and premixed counter-
flow CH4 flames were simulated using several detailed reaction mechanisms. The analysis systematically varied
the oxidizer composition (from air to pure oxy-fuel), equivalence ratio (Φ), inlet temperature, pressure, and strain
rate. The correlation between the CH2O ⋅ OH profiles and the HRR was quantified using the Pearson correlation
coefficient. Overall, the study confirms CH2O ⋅ OH as a practical and reliable HRR marker in CH4 flames, particu
larly under high-temperature and high-pressure oxy-fuel conditions. The results show that in slightly rich CH4-air
flames (about Φ = 1.5), CH2O ⋅ OH provides an accurate estimate of the HRR. In oxygen-enriched and pure oxy-
fuel flames, the strongest correlation shifts to ultra-rich conditions (about Φ ≈ 3.0). Increasing pressure enhances
the correlation at lower equivalence ratios, whereas preheating weakens the correlation locally but expands the
overall range of validity. Although increasing strain rate generally degrades the correlation, CH2O ⋅ OH remains a
robust marker under technically relevant turbulent conditions (strain rates below 10,000 s−1), maintaining Pearson
correlation coefficients of approximately ≈ 0.9 even close to extinction. This demonstrates the strong feasibility
of using CH2O ⋅ OH for HRR estimation in highly turbulent flames. The demonstrated robustness of CH2O ⋅ OH
highlights its suitability as an HRR marker, providing valuable insights for experimental diagnostics