Hochschule Bonn-Rhein-Sieg
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Conceptual Studies of a Smart, Self-Sufficient Energy System Utilizing Modular Multiport Converter for Connecting Green Generators via a DC Bus
No full textFrom Resistance to Retention: Passive Innovation Resistance and Post-Adoption Usage of Digital Services
No full textGlobal Social Protection: Institutional Perspectives
No full textThis innovative book provides a multidisciplinary institutional approach to social protection, combining insights from economics, law, philosophy, political science, and sociology. It examines the role of institutions in the effective functioning of social protection systems and explores the factors driving or hindering institutional change
Open RAN: A Concise Overview
Get PDFOpen RAN has emerged as a transformative approach in the evolution of cellular networks, addressing challenges posed by modern applications and high network density. By leveraging disaggregated, virtualized, and software-based elements interconnected through open standardized interfaces, Open RAN introduces agility, cost-effectiveness, and enhanced competition in the Radio Access Network (RAN) domain. The Open RAN paradigm, driven by the O-RAN Alliance specifications, is set to transform the telecom ecosystem. Despite extensive technical literature, there is a lack of succinct summaries for industry professionals, researchers, and policymakers. This paper addresses this gap by providing a concise, yet comprehensive overview of Open RAN. Compared to previous work, our approach introduces Open RAN by gradually splitting up different components known from previous RAN architectures. We believe that this approach leads to a better understanding for people already familiar with the general concept of mobile communication networks. Building upon this general understanding of Open RAN, we introduce key architectural principles, interfaces, components and use-cases. Moreover, this work investigates potential security implications associated with adopting Open RAN architecture, emphasizing the necessity of robust network protection measures
Analytical Chemistry II
No full textThis workbook takes you through the successful textbook Skoog/Holler/Crouch, Instrumentelle Analytik and is designed primarily for self-study.In five parts, the lecture content of more advanced analytical chemistry is summarized and explained using selected examples: mass spectrometry and nuclear magnetic resonance spectroscopy deal with the investigation of molecules, and numerous electroanalytical methods such as potentiometry, coulometry, amperometry and voltammetry are also covered. An overview of more specialized analytical methods includes the use of radioactive substances and various fluorescence methods, as well as methods of information acquisition in the increasingly important electrochemical and optical sensor technology and their automation. The course concludes with a summary of various principles and application methods of statistics, which are simply indispensable in the context of analytics. In order to facilitate independent learning, references to essential sections and illustrations of the textbook are made throughout the book
Supercritical fluid chromatography versus high-performance liquid chromatography – Performance, robustness and selectivity in the separation of vitamin A acetate and related substances
No full textApplication of a Regional Data Set of the Housing Sector for Hydrogen Storage-Supported Energy System Planning
Get PDFGermany aims to achieve a national climate-neutral energy system by 2045. The residential sector still accounts for 29% of end energy consumption, with 74% attributed to the direct use of fossil fuels for heating and hot water. In order to reduce fossil energy use in the household sector, great efforts are being made to design new energy concepts that expand the use of renewable energies to supply electricity and heat. One possibility is to convert parts of the natural gas grid to a hydrogen-based gas grid to deliver and store energy for urban quarters of buildings, especially with older building stock where electrification of heat via heat pumps is difficult due to technical, acoustical, and economic reasons. A comprehensive dataset was generated by a bottom-up analysis with open governmental and statistical data to determine regional building types regarding energy demand, solar potential, and existing grid infrastructure. The buildings’ connections to the electricity, gas, and district heating networks are considered. From this, a representative sample dataset was chosen as input for a newly developed energy system model based on energy flow simulation. The model simulates the interaction of hydrogen generation (HG) (from excess solar energy by electrolysis), storage in a metal-hydride storage (MHS) tank, and hydrogen use in a connected fuel cell (FC), forming a local PVPtGtHP (Photovoltaic Power-to-Gas-to-Heat-and-Power) network. Next to the seasonal hydrogen storage path (HSP), a battery will complete the system to form a hybrid energy storage system (HESS). Paired with seasonal time series for PV power, electricity and heat demand, and a model for connection to grid infrastructure, the simulation of different hydrogen applications and MHS placements aims to analyze operating times and energy share of the systems’ equipment and existing infrastructure. The method to obtain the data set together with the simulation model presented can be used by energy planners for cities, communities, and building developers to analyze the potentials of a quarter or region and plan a transition towards a more energy-efficient and sustainable energy system
DKZ.2R Data Competence College (DCC) - March 2025
No full textThe first Data Competence College was hosted from March 27th to 28th, 2025 at the IT center of RWTH Aachen. Based on the concept of the Wissenschaftskolleg in Berlin or the Institute of Advanced Studies in Princeton, we invited two individuals with high data competence from different scientific fields (“Data Experts”) to participate as part of the data competence college:
Prof. Sebastian Houben (Hochschule Bonn-Rhein-Sieg, specialist in AI and autonomous systems)
Dr. Moritz Wolter (University of Bonn, expert in high performance computing and machine learning)
For two days we aimed to create a space where not only local scientists, and especially early career researchers, learn from the data experts and each other regarding research data and methods but also data experts could inspire each other. The schedule included keynote presentations by all data experts, poster and group presentations by the participants, 1:1 sessions between data experts and early career researchers, as well as a method- and data-related workshop. We aimed foremost to create an environment in which everyone feels safe to give input, share their knowledge and learn from the other participants and experts
Reduction of Outflow Boundary Influence on Aerodynamic Performance using Neural Networks
No full textThe accurate treatment of outflow boundary conditions remains a critical challenge in computational fluid dynamics when predicting aerodynamic forces and/or acoustic emissions. This is particularly evident when employing the lattice Boltzmann method (LBM) as the numerical solution technique, which often suffers from inaccuracies induced by artificial reflections from outflow boundaries. This paper investigates the use of neural networks (NN) to mitigate these adverse boundary effects and enable truncated domain requirements. Two distinct NN-based approaches are proposed: (1) direct reconstruction of unknown particle distribution functions at the outflow boundary; and (2) enhancement of established characteristic boundary conditions (CBC) by dynamically tuning their parameters. The direct reconstruction model was trained on data generated from a 2D flow over a cylindrical obstruction. The drag, lift, and Strouhal number were used to test the new boundary condition. We analyzed results for various Reynolds numbers and restricted domain sizes where it demonstrated significantly improved predictions when compared with the traditional Zou & He boundary condition. To examine the robustness of the NN-based reconstruction, the same condition was applied to the simulation of a NACA0012 airfoil, again providing accurate aerodynamic performance predictions. The neural-enhanced CBC were evaluated on a 2D convected vortex benchmark and showed superior performance in minimizing density errors compared to CBCs with fixed parameters. These findings highlight the potential of NN-integrated boundary conditions to improve accuracy and reduce computational expense of aerodynamic and acoustic emissions simulations with the LBM