KITopen
Not a member yet
63711 research outputs found
Sort by
Microstructural and mechanical investigations regarding the formability of glass fiber reinforced thermoplastic pultruded profiles
Get PDFThermoplastic fiber reinforced composites have crucial benefits over thermoset composite materials regarding sustainability and reusability, as well as in post-processing, like welding or forming. In this study in-situ pultruded unidirectional reinforced glass fiber reinforced anionic polyamide composites are investigated for their formability, to be used as local stiffening elements in light weight over molded polymer matrix composites. A forming setup was designed and manufactured and a systematic study on forming parameters was carried out in a bending radius ranging from 10 to 30 mm, pre-heating temperature from 180 to 235°C and a fiber volume content from 60 to 70 vol -%. The formed profiles were investigated regarding their stiffness in a bridged apex flexure test and a cantilever flexure test and the microstructure of the composite with microscopy and computed tomography. The ideal forming parameters were found to be 20 mm bending radius, 70 vol.-% glass fiber content and 215°C pre-heating temperature of the profiles, for the forming setup used. For forming with lower radius and temperatures, the profiles showed fiber buckling, undulations and folded fiber bundles up to fiber breakage in the inner of the formed radius. For higher temperatures, degradation on the profile surface got visible and squeeze out effects, reducing the profile shape quality. This led to lower mechanical properties and higher scatter of values. The findings give insights to process optimization for forming thermoplastic pultrusion profiles and help to prevent pre-damage during manufacturing. With this, the study participates in making fiber reinforced polymer matrix composites more sustainable and in the green transformation of structural light-weight materials
Understanding bioreceptivity of concrete: realistic and accelerated weathering experiments with model subaerial biofilms
Get PDFVertical greening systems are a promising solution to the increasing demand for urban green spaces, improving environmental quality and addressing biodiversity loss. This study facilitates the development microbially greened algal biofilm facades, which offer a low maintenance vertical green space. The study focuses on concrete as a widely used building material and explores how physical surface characteristics impact its bioreceptive properties. Concrete samples, produced from the same mix but differing in surface structure, were subjected to a laboratory weathering experiment to assess their bioreceptivity. A novel inoculation method was employed, involving a single initial inoculation with either alga (Jaagichlorella sp.) alone, or a model biofilm consisting of a combination of the alga (Jaagichlorella sp.) with a fungus (Knufia petricola). The samples underwent four months of weathering in a dynamic laboratory setup irrigated with deionized water to observe subaerial biofilm attachment and growth. The formation of subaerial biofilms was monitored with high resolution surface imaging, colorimetric measurements and Imaging Pulse Amplitude Modulated Fluorometry (Imaging PAM-F), with Imaging PAM-F proving the most effective. Statistical analysis revealed that by impacting surface pH value and water retention capability, surface structures significantly influence microbial growth and that the concrete’s bioreceptivity can be influenced through thoughtful design of the materials surface. The inoculation of algae combined with a fungus facilitated the formation of a stable subaerial biofilm, enabling algae to colonize a surface structure that it could not colonize alone. This finding highlights the importance of modelling synergistic interactions present in natural biofilms
Functional morphology of the leg musculature in the marine seal louse: adaptations for high-performance attachment to diving hosts
Get PDFThe seal louse (Echinophthirius horridus) is a remarkable example of evolutionary adaptation, thriving as an obligate ectoparasite on deep-diving marine mammals under extreme environmental conditions, including high hydrostatic pressure, extreme drag force, salinity, and fluctuating temperatures. To investigate the anatomical and functional specializations enabling this lifestyle, we compared the leg morphology and musculature of E. horridus with its terrestrial relative, the human head louse (Pediculus humanus capitis), using synchrotron-based 3D microtomography and confocal laser scanning microscopy. Our findings reveal that the seal louse has developed a highly compact and robust leg structure with a fused tibiotarsus, an additional set of leg muscles, and a shortened claw tendon—an unprecedented adaptation among insects. These features allow for greater force transmission and reduced metabolic cost during sustained attachment. Behavioral assays further show that E. horridus can only move effectively on hair-like substrates, underscoring its complete reliance on host fur. These findings suggest a highly specialized muscular control system enabling strong, reliable, and reversible attachment in a challenging aquatic environment
Particle localization with DPTV and sizing with IPI using a forward model and optimization
Get PDFAn inverse problem (IP) approach is proposed to simultaneously determine the three-dimensional position and size of bubbles or droplets in a two phase flow from a single camera image. The method is based on interferometric particle imaging (IPI) and defocusing particle tracking velocimetry. A forward model (FM) is introduced that integrates a scattering model based on geometrical optics and the Lorentz–Mie theory, along with a wave propagation model based on the Huygens–Fresnel principle to simulate particle images. Using bounding boxes from object detection methods as initialization, the InvP approach approximates the position and diameter of each particle in the image. The performance of the presented approach is evaluated on the grounds of the data achieved by Sax et al (2025 Phys. Rev. Appl. 24 044083): As key aspects it achieves sub-pixel accuracy in position determination, exceeds the diameter accuracy of current FFT-based benchmarks on real data and furthermore achieves sub-micrometre precision in diameter resolution, even for three-dimensionally distributed particles. The InvP approach achieves a decoupling of the diameter estimation from the out-of-plane position estimation, thus avoiding error propagation from one to the other, which significantly increases the sizing accuracy. The incorporated FM accounts for aliasing effects in the interference pattern, effectively increasing the measurable volume both closer to and further from the focal plane. This improvement qualifies the approach to measure closer to the focal plane, which in turn allows to obtain images with higher signal-to-noise ratio (SNR). The InvP approach is capable of handling significantly lower SNRs compared to commonly applied algorithms and noise levels at which detection algorithms typically fail, presenting significant potential for single optical access IPI in side- and backscatter regions where low SNR usually necessitates sophisticated data processing methods. Notably, the InvP approach is largely unaffected by particle image overlaps, addressing another major challenge in single-camera particle tracking and sizing at high source densities in a given field of view
GeoLaB – the URL for Geothermal Energy
Get PDFGeoLaB (Geothermal Laboratory in the Crystalline Basement) constitutes a novel underground research infrastructure (URL), presently in its exploration and confirmation of site suitability phase, tailored to investigate coupled thermal, hydraulic, mechanical, and chemical (THMC) processes in fractured crystalline rock and advance understanding and implementation of Enhanced Geothermal Systems (EGS). Located in Germany, GeoLaB aims to bridge the gap between laboratory research and field-scale geothermal applications. This paper presents the scientific exploration progress, strategic development, and project management framework of GeoLaB. As a collaborative initiative between leading Helmholtz Centres and academic partners, GeoLaB represents a cornerstone for Europe’s sustainable heating transition and international geothermal innovation. Through its interdisciplinary approach and integration of digital technologies, the project will contribute to safer, more efficient geothermal development and fosters global partnerships to advance renewable energy science
Strengthening mechanism in Al–Cu–Li alloy processed by friction consolidation followed by high-pressure torsion
Get PDFThe primary objective of this study is to explore the precipitation behavior of Al–Cu–Li alloy powder processed through a two-step approach: friction consolidation (FC) followed by high-pressure torsion (HPT). Microstructure analysis by scanning electron microscope shows a refined microstructure after FC, with a further reduction in grain size following HPT. X-ray diffraction analysis confirmed the formation of T, T, and precipitates after FC, which persisted even after HPT. Small-angle X-ray scattering shows a reduction in the volume fraction of larger precipitate particles after HPT, while the smaller grain volume fraction increased. Additionally, the volume fraction of precipitates decreased as a function of strain. To understand the contributions of various mechanisms to an enhanced hardness observed after HPT, a physical model was employed. This study explores how HPT influences dislocation behavior, precipitation, and grain size, highlighting its role in tailoring the microstructure and properties of the friction consolidated Al–Cu–Li alloy
Why Should Urban Debris Dynamics Be Considered in Urban Flood Management?
Get PDFClimate change, urbanization, and inadequate infrastructure exacerbate urban flood risks, yet one critical factor remains largely overlooked: hazardous debris such as cars, construction materials, wood, plastic containers among others. In the Valencia 2024 flood alone, the Spanish Insurance Compensation Consortium reported about 144,000 vehicles damaged or destroyed, many of them mobilized by the flow, which demonstrates the scale of large-debris impacts during floods. Debris alters and intensifies flooding impacts by clogging drainage systems and streets, decreasing flow conveyance, and causing direct damage to infrastructure, lives, and ecosystems. Nevertheless, debris dynamics are largely absent from flood risk assessments and management strategies. This Commentary highlights the urgent need to integrate debris considerations into urban flood planning and emergency response. Using case studies from recent catastrophic floods, we illustrate how debris amplifies hazard. We explore emerging scientific insights into the influence of debris in different flood types (flash, fluvial, coastal, tsunamis), and discuss why current management strategies fail to incorporate this factor. A solution-oriented roadmap is possible and we propose an actionable strategy toward the integration of debris into flood risk management, contributing to adapting cities toward higher levels of safety and resilience
Anomaly Detection for Autonomous Driving
Get PDFWith small fleets of autonomous vehicles of SAE level 4, i.e., such without a safety driver, publicly available, the adoption of autonomous vehicles will only continue to increase. Embedded within shared mobility solutions, this technical advancement can lead to a more sustainable, safe, and comfortable future. Scaling autonomous vehicles more broadly, however, requires handling a wide variety of challenging scenarios, especially those with often rare anomalies. With rising fleet sizes, such scenarios appear with increasing frequency. As many Machine Learning systems follow a closed-world assumption based on a set of known classes, such unknowns remain challenging.
This dissertation addresses anomaly detection for autonomous driving from a holistic perspective, contributing to the generation of scenarios with anomalies, the detection of anomalies, and the handling of anomalies. The first part addresses external anomalies, i.e., such that occur in the environment. Generating scenarios involves providing normal data to train models and creating scenarios with anomalies to evaluate anomaly detection methods. Based on a theoretical systematization of anomalies from the literature, scenarios from all anomaly layers can be created. As generating such external anomalies is often dangerous or infeasible, data is provided through a simulation engine. Based on these scenarios, an anomaly detection method is presented, which is trained on unlabeled sensor data alone. It leverages a world model as a representation of normality, utilizing both camera and LIDAR data. Once detected, anomalies can be integrated into the training process of Neural Networks, removing their status as anomalies. The presented approach handles previously detected anomalies where controlled traffic rule exceptions are required. To achieve this, a situation-aware reward for Reinforcement Learning is introduced.
Next to challenges induced by external anomalies, the driving task can be equally impacted by internal anomalies, such as model failures. This dissertation contributes to the field of internal anomaly detection by detecting model failures without the need for labeled evaluation sets. This is achieved by analyzing the disagreements between two models trained on the same task, but with different learning paradigms. Based on real-world data, the method successfully reveals categorical model failures, most often in seemingly normal situations.
Summarizing, this dissertation presents a holistic set of contributions to the field of anomaly detection for autonomous driving, addressing the generation, detection, and handling of anomalies. This is emphasized by examining both internal and external anomalies
Aerosol effects on convective storms under pseudo-global warming conditions: insights from case studies in Germany
Get PDFGermany is heading toward a future with warmer temperatures due to climate change, and potentially cleaner air from electrification and stricter emission regulations. But how will these evolving environmental conditions affect severe convective storms? This study addresses this question by simulating three supercell events in high resolution using the ICOsahedral Non-hydrostatic (ICON) model. The events observed during the Swabian MOSES field campaigns in 2021 and 2023 are analysed using the pseudo-global warming approach to assess their evolution in a warmer climate. The effects of aerosols on clouds and precipitation were considered using a two-moment microphysics scheme in four temperature rise scenarios, providing detailed insights into the underlying microphysical mechanisms. The results indicate that higher temperatures generally enhance convection, resulting in more intense convective cells, increased precipitation amounts, and more extreme rainfall and hail events. Additionally, warmer conditions increase the likelihood of supercell formation and more intense mesocyclones. In some cases, precipitation increases exceed 7 % K, indicating super-Clausius–Clapeyron scaling and suggesting that additional dynamical and microphysical processes amplify rainfall beyond thermodynamic expectations. An important finding is that hailstones grow larger under lower cloud condensation nuclei (CCN) concentrations, and the area affected by large hail expands by up to 400 %, indicating growing severity and reach of hail events. In addition, lower CCN concentrations are associated with a reduced cold-to-warm rain formation ratio and decreased precipitation efficiency. These aerosol-related effects appear largely independent of temperature, showing consistent patterns across all simulated warming scenarios. These findings indicate the intensity of severe weather events, such as convective storms and flash floods, may increase in a future climate
On the Efimov effect in systems of one- or two-dimensional particles
Get PDFWe study virtual levels of -particle Schrödinger operators and prove that if the particles are one-dimensional and , then virtual levels at the bottom of the essential spectrum correspond to eigenvalues. The same is true for two-dimensional particles if . These results are applied to prove the non-existence of the Efimov effect in systems of one-dimensional or two-dimensional particles