Karlsruhe Institute Research Articles

Measurement report: The Fifth International Workshop on Ice Nucleation phase 1 (FIN-01): intercomparison of single-particle mass spectrometers

Abstract. Knowledge of the chemical composition and mixing state of aerosols at a single-particle level is critical for gaining insights into atmospheric processes. One common tool to make these measurements is single-particle mass spectrometry. There remains a need to compare the performance of different single-particle mass spectrometers (SPMSs). An intercomparison of SPMSs was conducted at the Aerosol Interaction and Dynamics in the Atmosphere (AIDA) chamber at the Karlsruhe Institute of Technology (KIT) in November 2014, as part of the first phase of the Fifth International Workshop on Ice Nucleation (FIN-01). In this paper we compare size distributions and mass spectra of atmospherically relevant particle types measured by five SPMSs. These include different minerals, desert and soil dusts, soot, bioaerosol (Snomax; protein granule), secondary organic aerosol (SOA), and SOA-coated mineral particles. Most SPMSs reported similar vacuum aerodynamic diameter (dva) within typical instrumental ranges from ∼100–200 nm (lower limit) to ∼2–3 µm (upper limit). In general, all SPMSs exhibited a wide dynamic range (up to ∼103) and high signal-to-noise ratio (up to ∼104) in mass spectra. Common spectral features with small diversities in mass spectra were found with high average Pearson's correlation coefficients, i.e., for average positive spectra ravg-pos=0.74 ± 0.12 and average negative spectra ravg-neg=0.67 ± 0.22. We found that instrument-specific detection efficiency (DE) was more dependent on particle size than particle type, and particle identification favored the use of bipolar, rather than monopolar, instruments. Particle classification from “blind experiments” showed that all instruments differentiated SOA, soot, and soil dust and detected subtle changes in the particle internal mixing but had difficulties differentiating among specific mineral types and dusts. This study helps to further understand the capabilities and limitations of the single-particle mass spectrometry technique in general and the specific performance of the instrument in characterizing atmospheric aerosol particles.

Strategic boundary management in university‐based living labs

AbstractUniversity‐based boundary organisations provide academics with an environment where they can interact with a wide variety of societal partners to produce knowledge and work on research projects, often of a transdisciplinary nature. This environment, however, implies that their researchers may be confronted with multiple and sometimes conflicting demands coming from various stakeholders. In this study, we focus on one such case, a Real‐world Laboratory (RwL), set up by the Karlsruhe Institute of Technology. This RwL focuses on urban sustainable development challenges. Drawing on boundary work literature, we analyse the boundary work strategies employed by the lab's leading researchers to manage such demands as part of a RwL research project. We observe that the managing researchers buffer several types of boundaries in order not to compromise the transdisciplinary nature of the RwL. They appear to utilise four types of boundary devices when managing boundaries: language, people, objects, and spaces. We conclude that, to reduce tensions, the managing researchers should acknowledge the presence of boundaries early on in their RwL research project, that is, during its conception phase.

Technical Aspects of Natural Gas Pyrolysis in Liquid Metal Bubble Column Reactors

The pyrolysis of low alkanes (in the following short “pyrolysis”) has already been investigated during the 1960s. However, none of the reactor systems used at the time are capable of continuous operation. Therefore, the Karlsruhe Institute of Technology has intensified the development of the promising liquid metal bubble column technology in recent years, which is capable of continuous operation. Various key aspects have been addressed, such as scale‐up and the pyrolysis of high‐caloric natural gas. Herein, further developments for a pilot scale system have been investigated, which concern increased throughput and long‐term operation capabilities. Careful evaluation of the impact of according measures has been done, which shows that the achieved scale‐up has only negligible effects on the pyrolysis outcome. The effects of the scale‐up on residence times are negligible. The bubble formation behavior depends on the throughput and the characteristics of the orifice. Wall effects are marginal. Fundamental minimization of weeping could not be confirmed. Reactor pre‐chambers in combination with tin collection chambers are recommended for further scale‐up. An increase in the volume flow should be examined. In terms of long‐term operation , head as well as feed pressure control is recommended.

Z-YOLOv8s-based approach for road object recognition in complex traffic scenarios

Object detection in road scenarios is crucial for intelligent transport systems and autonomous driving, but complex traffic conditions pose significant challenges. This paper introduces Z-You Only Look Once version 8 small (Z-YOLOv8s), designed to improve both accuracy and real-time efficiency under real-world uncertainties. By incorporating Revisiting Perspective Vision Transformer (RepViT) and C2f into the YOLOv8s framework, and integrating the Large Selective Kernel Network (LSKNet), the model enhances spatial feature extraction. Additionally, the YOLOv8s backbone is optimized with Space-to-Depth Convolution (SPD-Conv) for better small object detection. The Softpool-Spatial Pyramid Pooling Fast (SoftPool-SPPF) module ensures precise characteristic information preservation. Z-YOLOv8s improves mean average precision (mAP)@0.5 on the Berkeley Deep Drive 100 K (BDD100K) and Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) datasets by 7.3 % and 3.8 %, respectively. It also achieves accuracy increases of 5.7 % and 6.5 % in Average Precision (AP)-Small, and a real-time detection speed of 78.41 frames per second (FPS) on the BDD100K. Z-YOLOv8s balances detection precision and processing speed more effectively than other detectors, as demonstrated by experimental results and comparisons.

Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion

Depth estimation represents a prevalent research focus within the realm of computer vision. Existing depth estimation methodologies utilizing LiDAR (Light Detection and Ranging) technology typically obtain sparse depth data and are associated with elevated hardware expenses. Multi-view image-matching techniques necessitate prior knowledge of camera intrinsic parameters and frequently encounter challenges such as depth inconsistency, loss of details, and the blurring of edges. To tackle these challenges, the present study introduces a monocular depth estimation approach based on an end-to-end convolutional neural network. Specifically, a DNET backbone has been developed, incorporating dilated convolution and feature fusion mechanisms within the network architecture. By integrating semantic information from various receptive fields and levels, the model’s capacity for feature extraction is augmented, thereby enhancing its sensitivity to nuanced depth variations within the image. Furthermore, we introduce a loss function optimization algorithm specifically designed to address class imbalance, thereby enhancing the overall predictive accuracy of the model. Training and validation conducted on the NYU Depth-v2 (New York University Depth Dataset Version 2) and KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) datasets demonstrate that our approach outperforms other algorithms in terms of various evaluation metrics.

Promoting dual careers at higher education institutions: 31 benefits ranked by the project Student Athletes Erasmus+ Mobility in Europe (SAMEurope).

The project Student Athletes Erasmus+ Mobility in Europe identified and defined a total of 31 benefits offered to dual-career student athletes who are combining their university studies with high-level training. The project was co-funded by the European Union and carried out by five universities: Chalmers University of Technology (Sweden), Institut national des sciences appliquées de Lyon (France), University of Jyväskylä (Finland), Karlsruhe Institute of Technology (Germany), and Universitat Jaume I (Spain). The purpose of the study was to rank these benefits by combining the perspectives of the university staff and the student athletes from each university in the consortium. The university staff included experts from sports services and the international relations office. A questionnaire was also sent to the dual-career athletes enrolled at the consortium's universities. Of the 514 dual-career athletes, 208 (116 women) completed the questionnaire. The overall response rate was 40.47%. The university staff assessed the importance of each benefit, how easy it was to implement at the institution, and whether or not the university offered the benefit to its students. The dual-career students rated each of the 31 benefits and indicated whether or not they had used them. A specific methodology was designed to rank these benefits using the ratings of the university staff and the student athletes. Intra-group and inter-group Pearson correlations were performed. The results show a strong and significant correlation between the benefits from the perspective of the university staff (r = 0.710, p = 7.76E-7) and from the perspective of the students (r = 0.715, p = 2.44E-6). The correlation is moderate and significant when the benefits are correlated from the perspective of the two groups as a whole (0.363, p = 0.045), with the three most important benefits being the free use of sports facilities, justification for absences, and the adaptation of the pace of studies. The study makes visible the commitment of higher education institutions to facilitating the dual career of student athletes and identifies those benefits that may be of greater interest to European universities as a whole. The European perspective has been considered, while respecting the specificities of each university and the country in which it is located.

AN OVERVIEW AND ANALYSIS OF SILENT SOUND TECHNOLOGY

‌Nowadays whenever we are in a crowd or there was a to much background noise around us then it is very difficult to communicate or interact over the phone, but now we have an awesome solution on this problem that is ' silent sound technology ' it is a gift for those who lost their voice but wish to speak over the phone. this technology is produced by the Karlsruhe institute of technology, Germany and it will be available soon in the market. Silent sound technology is also known as ultrasonic sound or ultrasonic communication enables the transmission of data through inaudible sound waves. in this technology it detects every lip movement and convert it into the sound signal neglecting all background noise. without using real audio it is a visual interpretation of lip, mouth and face. So basically it reads our lip. This technology is available in English, German and French language, but not in language like Chinese because different tones hold different meanings in Chinese language. it use to allow silent calls and we can share our personal information like PIN , password or secret information to trusted one without knowing any other. it is also use in the Military for the secret missions. According to the researchers the technology have 99% efficiency only 1% possibility that it was produce a incorrect voice and upcoming 6-7 years the normal peoples are also use this technology because it launching very soon. Keywords : silent sound technology, electromyography, image processing.

Preface

The 2023 Joint Cryogenic Engineering Conference (CEC) and International Cryogenic Materials Conference (ICMC) were held from July 9 through July 13 at the Hawaii Convention Center in Honolulu, Hawaii. As at past conferences, the international scope of these meetings was strongly maintained with 19 countries being represented by 555 attendees who gathered to enjoy the joint technical programs and industrial exhibits. In total, 213 papers were submitted for publication of which 207 are published in these conference proceedings.The program for the joint conferences included a total of 494 presentations organized into 110 sessions - plenary, oral and posters, as well as awards presentations. Five plenary talks gave interesting in-depth updates and overviews on exciting topics: Amanda Simpson (Airbus Americas) discussed the Airbus hydrogen aircraft development plans in “Airbus: A Discussion About the Use of Cryogenic Hydrogen for Aviation Propulsion”, An overview of the history and recent developments in high temperature superconductors was given by Paul C. W. Chu (University of Houston) in “From High-temperature Superconductivity to Room-temperature Superconductivity: From Ambient Pressure to Very High Pressure”, Suhas Bhandarkar (Lawrence Livermore National Laboratory) presented an overview of Lawrence Livermore National Lab’s net positive energy fusion demonstration and the detailed development and operations of the hydrogen fuel in “Overview of the Fabrication of Cryogenic Hydrogen Fuel Ice Layer for Nuclear Fusion Ignition Experiments”, an overview of the developments ongoing at Robinson Research Institute for the development of electric aircraft was given by Rodney Badcock (Robinson Research Institute) in “Electric Aircraft: Solving the Key Materials and Engineering Challenges to Manufacture a Complete Electrical Propulsion System”, and a discussion on the cryogenic requirements and needs for the development of quantum computers was given by Nicholas Masluk (IBM) in “Cryogenic Infrastructure for 400 Qubits and Beyond”. The attendees also convened for two special Joint CEC-ICMC sessions organized: i) Liquid Hydrogen for Large-Scale Vehicles and ii) Superconducting Quantum Systems. Special sessions, two of them with panel discussions, were included in the programming emphasizing high-quality invited talks on seven different topics: Electromechanical Behaviors of HTS Coated Conductors for Applications, Topological Materials for Electronics, High Strength Materials, High Field Superconducting Materials for Accelerator Magnets, Low Temperature Materials Database A - Superconductors, Low Temperature Materials Database B - Functional & Structural Materials, Cryogenic Clean Energy and Mobility, and Orbital Flight Demonstrations of Cryogenic Fluid Management. A large Transportation Symposium consisting of 5 sessions and 26 talks with topics ranging from government funding perspectives, power electronics, cables, motors, and generators to system level studies was also held at CEC/ICMC’23. Contributed papers covered a wide range of topics including many aspects and advances in cryogenics and superconductors, along with their applications.Both CEC and ICMC boards are encouraging student participation, education, and career growth. It was very exciting and rewarding to have 77 students register for the conference this year. And 36 of the students received sponsorship of the registration fees. The strong session attendance was exciting and very positive and made the conference a more rewarding experience for the presenters and attendees.The CEC/ICMC Cryo Industrial Expo displayed the products and services of 31 industrial exhibitors and provided a congenial venue for a reception and refreshments throughout the week as well as for the conference poster sessions.Conference Chairs for 2023 were Wesley Johnson from the NASA Glenn Research Center for CEC and Sonja Schlachter from the Karlsruhe Institute of Technology for ICMC. The CEC Program Chair was Jacob Leachman from the Washington State University with CEC Program Vice Chair, Ram Dhuley, from the Fermi National Accelerator Laboratory and the ICMC Program co-chairs were Klaus-Peter Weiss from the Karlsruhe Institute of Technology and Judy Wu from the University of Kansas. The Exhibit Chair was Austin Capers of Scientific Instruments, Inc. who also served as Publicity and Sponsorship Chair. Finally, the Awards Chairs were Peter Kittel, Consultant, for CEC and Eric Hellstrom from the National High Magnetic Field Laboratory at Florida State University for ICMC. Michael Sumption, ICMC Board President and Peter Bradley, CEC Board President greatly participated in the conference development as part of the leadership team.

ICMC Board of Directors

2023 International Cryogenic Materials Conference Board of Directors CONFERENCE AND PROGRAM Michael Sumption, President – The Ohio State UniversitySonja Schlachter, Conference Chair – Karlsruhe Institute of Technology, GermanyKlaus-Peter Weiss, Program Co-Chair – Karlsruhe Institute of Technology, GermanyJudy Wu, Program Co-Chair – University of Kansas BOARD OF DIRECTORS Shreyas Balachandran – Thomas Jefferson National Accelerator FacilityU. (Balu) Balachandran, Chief Technical Editor – University of Colorado-BoulderPeter Bradley – National Institute of Standards and TechnologyDavid A. Cardwell – University of Cambridge, United KingdomK. Theodore Hartwig, Jr. – Texas A&M UniversityTimothy J. Haugan – U.S. Air Force Research LaboratoryEric E. Hellstrom – NHMFL, Florida State UniversityTakanobu Kiss – Kyushu University, JapanYanwei Ma – Institute of Electrical Engineering, Chinese Academy of Science, ChinaRichard P. Reed – Cryogenic Materials, Inc.Kenichi Sato – Japan Science and Technology Agency, JapanSonja Schlachter – Karlsruhe Institute of TechnologyJun-ichi Shimoyama – Aoyama Gakuin University, JapanHyung-Seop Shin – Andong National University, South KoreaMichael D. Sumption – The Ohio State UniversityHerman H. J. ten Kate – University of Twente, NetherlandsRobert P. Walsh – NHMFL, Florida State UniversityKlaus-Peter Weiss – Karlsruhe Institute of Technology, GermanyJudy Wu – University of Kansas AWARDS COMMITTEE K. Theodore Hartwig, Jr. – Texas A&M UniversityEric E. Hellstrom, Chair – NHMFL, Florida State UniversityJun-ichi Shimoyama – Aoyama Gakuin University, JapanHyung-Seop Shin – Andong National University, South KoreaHerman H. J. ten Kate – University of Twente, Netherlands

Preface

The 2023 Joint Cryogenic Engineering Conference (CEC) and International Cryogenic Materials Conference (ICMC) were held from July 9 through July 13 at the Hawaii Convention Center in Honolulu, Hawaii. As at past conferences, the international scope of these meetings was strongly maintained with 19 countries being represented by 555 attendees who gathered to enjoy the joint technical programs and industrial exhibits. In total, 213 papers were submitted for publication of which 207 are published in these conference proceedings.The program for the joint conferences included a total of 494 presentations organized into 110 sessions - plenary, oral and posters, as well as awards presentations. Five plenary talks gave interesting in-depth updates and overviews on exciting topics: Amanda Simpson (Airbus Americas) discussed the Airbus hydrogen aircraft development plans in “Airbus: A Discussion About the Use of Cryogenic Hydrogen for Aviation Propulsion”, An overview of the history and recent developments in high temperature superconductors was given by Paul C. W. Chu (University of Houston) in “From High-temperature Superconductivity to Room-temperature Superconductivity: From Ambient Pressure to Very High Pressure”, Suhas Bhandarkar (Lawrence Livermore National Laboratory) presented an overview of Lawrence Livermore National Lab’s net positive energy fusion demonstration and the detailed development and operations of the hydrogen fuel in “Overview of the Fabrication of Cryogenic Hydrogen Fuel Ice Layer for Nuclear Fusion Ignition Experiments”, an overview of the developments ongoing at Robinson Research Institute for the development of electric aircraft was given by Rodney Badcock (Robinson Research Institute) in “Electric Aircraft: Solving the Key Materials and Engineering Challenges to Manufacture a Complete Electrical Propulsion System”, and a discussion on the cryogenic requirements and needs for the development of quantum computers was given by Nicholas Masluk (IBM) in “Cryogenic Infrastructure for 400 Qubits and Beyond”. The attendees also convened for two special Joint CEC-ICMC sessions organized: i) Liquid Hydrogen for Large-Scale Vehicles and ii) Superconducting Quantum Systems. Special sessions, two of them with panel discussions, were included in the programming emphasizing high-quality invited talks on seven different topics: Electromechanical Behaviors of HTS Coated Conductors for Applications, Topological Materials for Electronics, High Strength Materials, High Field Superconducting Materials for Accelerator Magnets, Low Temperature Materials Database A - Superconductors, Low Temperature Materials Database B - Functional & Structural Materials, Cryogenic Clean Energy and Mobility, and Orbital Flight Demonstrations of Cryogenic Fluid Management. A large Transportation Symposium consisting of 5 sessions and 26 talks with topics ranging from government funding perspectives, power electronics, cables, motors, and generators to system level studies was also held at CEC/ICMC’23. Contributed papers covered a wide range of topics including many aspects and advances in cryogenics and superconductors, along with their applications.Both CEC and ICMC boards are encouraging student participation, education, and career growth. It was very exciting and rewarding to have 77 students register for the conference this year. And 36 of the students received sponsorship of the registration fees. The strong session attendance was exciting and very positive and made the conference a more rewarding experience for the presenters and attendees.The CEC/ICMC Cryo Industrial Expo displayed the products and services of 31 industrial exhibitors and provided a congenial venue for a reception and refreshments throughout the week as well as for the conference poster sessions.Conference Chairs for 2023 were Wesley Johnson from the NASA Glenn Research Center for CEC and Sonja Schlachter from the Karlsruhe Institute of Technology for ICMC. The CEC Program Chair was Jacob Leachman from the Washington State University with CEC Program Vice Chair, Ram Dhuley, from the Fermi National Accelerator Laboratory and the ICMC Program co-chairs were Klaus-Peter Weiss from the Karlsruhe Institute of Technology and Judy Wu from the University of Kansas. The Exhibit Chair was Austin Capers of Scientific Instruments, Inc. who also served as Publicity and Sponsorship Chair. Finally, the Awards Chairs were Peter Kittel, Consultant, for CEC and Eric Hellstrom from the National High Magnetic Field Laboratory at Florida State University for ICMC. Michael Sumption, ICMC Board President and Peter Bradley, CEC Board President greatly participated in the conference development as part of the leadership team.

Curvature Scale Space LiDAR Odometry And Mapping (LOAM)

The LiDAR Odometry and Mapping (LOAM) algorithm ranks in second place in the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI), Visual Odometry/SLAM Evaluations. It utilizes a feature extraction algorithm based on the evaluation of the curvature of points under test, to produce estimated smooth and non-smooth regions within typically laser based Point Cloud Data (PCD). This feature extractor (FE) however, does not take into account PCD spatial or detection uncertainty, which can result in the divergence of the LOAM algorithm. Therefore, this article proposes the use of the Curvature Scale Space (CSS) algorithm as a replacement for LOAM’s current feature extractor. It justifies the substitution, based on the CSS algorithm’s similar computational complexity but improved feature detection repeatability. LOAM’s current feature extractor and the proposed CSS feature extractor are tested and compared with simulated and real data, including the KITTI odometry-laser data set. Additionally, a recent deep learning based LiDAR Odometry (LO) algorithm, the Convolutional Auto-Encoder (CAE)-LO algorithm, will also be compared, using this data set, in terms of its computational speed and performance. Performance comparisons are made based on the Absolute Trajectory Error (ATE) and Cardinalized Optimal Linear Assignment (COLA) metrics. Based on these metrics, the comparisons show significant improvements of the LOAM algorithm with the CSS feature extractor compared with the benchmark versions.

Using conditional Invertible Neural Networks to perform mid‐term peak load forecasting

AbstractMeasures for balancing the electrical grid, such as peak shaving, require accurate peak forecasts for lower aggregation levels of electrical loads. Thus, the Big Data Energy Analytics Laboratory (BigDEAL) challenge—organised by the BigDEAL—focused on forecasting three different daily peak characteristics in low aggregated load time series. In particular, participants of the challenge were asked to provide long‐term forecasts with horizons of up to 1 year in the qualification. The authors present the approach of the KIT‐IAI team from the Institute for Automation and Applied Informatics at the Karlsruhe Institute of Technology. The approach to the challenge is based on a hybrid generative model. In particular, the authors use a conditional Invertible Neural Network (cINN). The cINN gets the forecast of a sliding mean as representative of the trend, different weather features, and calendar information as conditioning input. By this, the proposed hybrid method achieved second place overall and won two out of three tracks of the BigDEAL challenge.

Experimental investigation of MHD flows in a WCLL TBM mock-up

Experiments have been performed in the MEKKA laboratory of the Karlsruhe Institute of Technology to characterize the influence of a magnetic field on liquid metal flows in a scaled mock-up of the water-cooled lead-lithium test blanket module of ITER. The test section consists of eight breeder units that are fed and drained by long electrically coupled manifolds oriented along the poloidal direction. Pressure differences between several points of the mock-up have been recorded for various liquid metal flow rates and strengths of the imposed magnetic field. The main contributions to the total pressure drop in the test section have been identified as a function of characteristic flow parameters. For sufficiently strong magnetic fields, the experimental data shows that the non-dimensional pressure loss is practically independent of the flow rate, i.e. inertia forces become negligible. The experiments also confirm previous conclusions of magnetohydrodynamic experiments performed in a scaled-down mock-up of a helium-cooled lead-lithium blanket, namely that the largest pressure drop in the blanket module originates from the flow in the distributing and collecting manifolds. Moreover, the experimental study demonstrates that the current manifold design does not allow the flow to be uniformly distributed among all the breeder units.

The Effect of Film Development on Primary Breakup in a Prefilming Airblast Atomizer

Abstract Liquid fueled gas turbines are likely to remain a dominant force in aviation propulsion for the foreseeable future, and therefore understanding the atomization process is key to meeting future emissions and performance legislation. To make experiments and simulations possible, simplified geometry and boundary conditions are often used, for example, simulations of primary atomization often use a fixed film height and velocity. This paper aims to quantify the effect of a fully developed unsteady film on the atomization process. A custom Coupled Level Set & Volume of Fluid (CLSVOF) solver with adaptive meshing built in OpenFOAM v9 is used. A simulation of the atomization process in the Karlsruhe Institute of Technology atomization experiment (Warncke et al., 2017, “Experimental and Numerical Investigation of the Primary Breakup of an Airblasted Liquid Sheet,” Int. J. Multiphase Flow, 91, pp. 208–224) is presented. A precursor simulation of the film development is used to provide accurate, temporally and spatially resolved inlet boundary conditions. These results are compared to previous CLSVOF simulations from Wetherell et al. (2020, “Coupled Level Set Volume of Fluid Simulations of Prefilming Airblast Atomization With Adaptive Meshing,” ASME Paper No. GT2020-14213)” using traditional boundary conditions. The unsteady film has doubled the modal ligament length and widened the distribution, and is now in better agreement with experimental measurements. A clear correlation in both time and space is observed between the film, atomization process, and spray. The sauter mean diameter (SMD) is significantly increased, again giving better agreement with the experiment. A discussion of extracting statistical descriptions of the spray is given, outlining the unfeasible computational cost required to converge droplet diameter distributions and other high order statistics for a case such as this.

Bending Behavior of Hybrid Timber-Steel Beams.

Driven by climate change and the need for a more sustainable construction sector, policy is increasingly demanding and promoting timber hybrid construction methods. In the German state of Baden-Württemberg, every new public building has to be of timber or timber hybrid construction (Holzbauoffensive BW). The objective of multi-story buildings with large floor spans can only be achieved in a resource-efficient way by hybrid constructions combining timber and steel components. A research project recently completed at the Karlsruhe Institute of Technology was aimed at the development and systematic investigation of hybrid bending beams in which an advantageous combination of the materials steel and timber is used. For this purpose, steel profiles are integrated into timber cross-sections in a shear-resistant manner by adhesive bonding. As part of the experimental, numerical and analytical investigations, different cross-sections of steel and timber, as well as different construction materials, were considered (GL24h, LVL48p, LVL80p, S355 and S420). The results of large-scale four-point bending tests illustrate the potential of this new hybrid construction method. Depending on the geometry and material combinations tested, the bending stiffness could be increased by up to 250%, and the load-carrying capacity by up to 120%, compared to a glulam beam with identical dimensions.

3D VAE Video Prediction Model with Kullback Leibler Loss Enhancement

The Video Prediction (VP) models adopted many techniques to build suitable structures to extract the spatiotemporal features and predict the future frame. The VP techniques extracted the spatial and temporal features in separated models and then fused both features to generate the future frame. However, these architectures suffered from the design complexity and time for prediction required. So, many efforts introduced VP based on decreasing design complexity and producing good results. This study produces the VP model based on a Three-Dimensional Variational Auto Encoder (3D VAE). The proposed model builds all layers depending on 3D convolutional layers. This leads to better extraction of spatiotemporal information and decreases the design complexity. Second, the Kullback Leibler Loss (KL Loss) is enhanced by a 3D sampling stage which allows to calculation of the 3D latent loss. This helps to extract the better and proper spatiotemporal latent variable from the 3D Encoder. The 3D sampling represents a good regularizer in the model. The proposed model outperforms in terms of SNR=34.8673, SSIM= 0.9616 which applied to Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) and Caltech pedestrian datasets and records 5.2 M parameters.

Quantitative Testing and Analysis of Non-Standard AEB Scenarios Extracted from Corner Cases

Existing testing methods for Automatic Emergency Braking (AEB) systems mostly rely on standard-based qualitative analysis of specific scenarios, with a focus on whether collisions occur. To explore scenarios beyond the standard conduct, a comprehensive testing model construction and analysis, and provide a more quantitative evaluation of AEB performance, this study extracted three typical hazardous driving scenarios from the KITTI (The Automated Driving dataset was created by the Karlsruhe Institute of Technology in Germany and the Toyota Institute of Technology in the United States) naturalistic driving dataset using kinematic data. A DME (Data Missing Estimation) scene construction method was proposed, and these scenarios were simulated and reconstructed in PRESCAN (PRESCAN is an automotive simulation software owned by Siemens, Munich, Germany). A C-AEB (Curve-Automatic Emergency Braking) testing model was developed and tested based on simulations. Finally, a BCEM (Boundary collision evaluation model) was proposed to quantitatively evaluate AEB performance. The focus of the analysis was on the identified cornering scenario A (severely failed AEB scenario). A C-AEB testing model was constructed based on the DME scene construction method for this cornering AEB failure scenario, and it was evaluated using the BCEM. The study found that the average performance degradation rate (performance degradation rate refers to the ratio of AEB performance in the current scenario compared to the standard straightaway test) of the AEB system in this cornering scenario reached 75.44%, with a maximum performance degradation rate of 89.47%. It was also discovered that the severe failure of AEB in this cornering scenario was mainly caused by sensor system perception defects and limitations of traditional AEB algorithms. This fully demonstrates the effectiveness of our testing and evaluation methodology.

Potential and Limitations of Research Battery Cell Types for Electrochemical Data Acquisition

Developing new electrode materials or electrolytes for lithium-ion batteries requires reliable electrochemical testing thereof. In academic research, coin-type cells and small pouch-type cells are commonly being assembled for this purpose. In our study [1], using commercial highly homogeneous and robust LiNi0.33Mn0.33Co0.33O2 (NMC111) cathodes, graphite anodes and 1M LiPF6 in EC/DMC with 3% VC as the electrolyte, four experienced researchers from the Institute for Applied Materials at Karlsruhe Institute of Technology assembled a variety of coin cells within a round robin test. For different cell building parameters, the battery lifetimes systematically varied from a few hundred to well over 2000 cycles before a capacity retention of 80 % was reached. As a benchmark, the same electrodes and electrolyte were incorporated as single- and multi-layer electrode stacks into pouch-type cells and reach almost 4000 cycles with a rate of 1 C.Missplacing of electrodes and varying amount of electrolyte within a coin-cell were already addressed as performance influencing factors in the literature. In this study, one clear outcome with high impact on previously collected electrochemical data on coin cells, is that the steel grade of cell parts, as well as the in-housed total height of the electrode stack have dramatic influence on cell lifetime and the quality of the acquired electrochemical data. We present our best practice for coin cell manufacturing resulting from the round robin test. Further, it is shown that inappropriate testing conditions like poor cell contacting and cycler settings have additional impact on data collection. In this regard, we emphasize essential parameters for obtaining high quality data.Additionally, using the same electrodes, we show direct rate performance differences of calendered vs. uncalendered electrodes, as well as half vs. full-cell test results by means of rate capability and long-term cycling stability. While compression of anodes is less crucial for rate capability, substantial differences are measured in case of the cathodes, highlighting the importance of sufficient cathode densification. Moreover, for long-term capacity retention studies, it is demonstrated that electrode densification of both, cathode and anode is crucial for high long-term capacity retention.Our results are bridging the gap between lab research and industry-type battery cells. It is shown how key performance parameters can be projected from corresponding lab scale battery cell data, if certain important criteria are met vs. where the projections have their limits.[1] A. Smith, P. Stüble, L. Leuthner, A. Hofmann, F. Jeschull and L. Mereacre, Batteries & Supercaps 2023, e202300080. Figure 1

An Attention-Based Odometry Framework for Multisensory Unmanned Ground Vehicles (UGVs)

Recently, deep learning methods and multisensory fusion have been applied to address odometry challenges in unmanned ground vehicles (UGVs). In this paper, we propose an end-to-end visual-lidar-inertial odometry framework to enhance the accuracy of pose estimation. Grayscale images, 3D point clouds, and inertial data are used as inputs to overcome the limitations of a single sensor. Convolutional neural network (CNN) and recurrent neural network (RNN) are employed as encoders for different sensor modalities. In contrast to previous multisensory odometry methods, our framework introduces a novel attention-based fusion module that remaps feature vectors to adapt to various scenes. Evaluations on the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI) odometry benchmark demonstrate the effectiveness of our framework.

Uncertainty and Sensitivity Evaluation of the QUENCH-02 Experiment Simulation Using the ASYST Code

Abstract Uncertainty and sensitivity methods are increasingly used in safety analyzes of nuclear power plants to address the unreliability of input data, numerical models, and, in general, the lack of knowledge regarding certain physical phenomena, in determining safety margins and acceptance criteria. The ASYST code, developed as part of an international nuclear technology ASYST Development and Training Program (ADTP) managed by Innovative Systems Software (ISS), is used to perform an uncertainty analysis of the QUENCH-02 experiment conducted at the Karlsruhe Institute of Technology. The code uses a probabilistic methodology based on the propagation of input uncertainties. The QUENCH facility contains electrically heated PWR fuel rod simulators and the aim of the experiment is to examine hydrogen generation and the behavior of the fuel rod cladding during core reflood. For selected input parameters, such as steam/water flow, electrical power, and other relevant boundary conditions, it is necessary to define their probability density functions. Input databases are then prepared for individual calculations based on the selected confidence level and confidence interval. The number of performed calculations is 60, large enough to ensure at least 95% coverage of expected output results and uncertainty limits. The results of the calculations are compared with the experimental measurements. The Pearson correlation coefficient is used to obtain correlation between the input uncertain parameters and the output data. Sensitivity analyses cover the influence of variations in the heater's electrical power and the steam flow rate on the hydrogen production and the maximum cladding temperature.