Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
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- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (568)
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- West Africa (7)
- lignin (7)
- advanced applications (6)
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- energy meteorology (5)
- modeling of complex systems (5)
- stem cells (5)
- Ghana (4)
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Investigating Temperature Variations in AC Impedance with an Indigenously Developed EIS Test-Bed
(2025)
In Ghana, unreliable public grid infrastructure greatly impacts rural healthcare, where diesel generators are commonly used despite their high financial and environmental costs. Photovoltaic (PV)-hybrid systems offer a sustainable alternative, but require robust, predictive control strategies to ensure reliability. This study proposes a sector-specific Model Predictive Control (MPC) approach, integrating advanced load and meteorological forecasting for optimal energy dispatch. The methodology includes a long-short-term memory (LSTM)-based load forecasting model with probabilistic Monte Carlo dropout, a customized Numerical Weather Prediction (NWP) model based on the Weather Research and Forecasting (WRF) framework, and deep learning-based All-Sky Imager (ASI) nowcasting to improve short-term solar predictions. By combining these forecasting methods into a seamless prediction framework, the proposed MPC optimizes system performance while reducing reliance on fossil fuels. This study benchmarks the MPC against a traditional rule-based dispatch system, using data collected from a rural health facility in Kologo, Ghana. Results demonstrate that predictive control greatly reduces both economic and ecological costs. Compared to rule-based dispatch, diesel generator operation and fuel consumption are reduced by up to 61.62% and 47.17%, leading to economical and ecological cost savings of up to 20.7% and 31.78%. Additionally, system reliability improves, with battery depletion events during blackouts decreasing by up to 99.42%, while wear and tear on the diesel generator and battery are reduced by up to 54.93% and 37.34%, respectively. Furthermore, hyperparameter tuning enhances MPC performance, introducing further optimization potential. These findings highlight the effectiveness of predictive control in improving energy resilience for critical healthcare applications in rural settings.
The growing interest toward biopolymers application in amphiphilic conditions prompts one to explore the preparation of fluorinated cellulosic materials. Cellulose (CE) and carboxymethylcellulose (CMC) are functionalized with highly fluorinated pendants, through a nucleophilic aromatic substitution on 3-pentadecafluoroheptyl-5-pentafluorophenyl-1,2,4-oxadiazole (FOX) leading to the corresponding fluorinated biopolymers CE-FOX and CMC-FOX. Structural and thermal stability confirm covalent attachment of the fluorinated moiety onto the cellulosic skeleton and highlighted an interesting 2D texture of the CMC-FOX material. Hybrid and amphiphilic features of CE-FOX and CMC-FOX, are confirmed by water and oil contact angle measurements. Applications as adsorbent material for organic contaminants from an aqueous solution are tested by previously incorporating the functional biopolymer into sodium alginate (SA) hydrogel beads. Rhodamine B (RhB) is used as a model wastewater pollutant. Fluoro-functionalization led to a three- to eightfold increase in the dye-removal efficiency of the SA-incorporated biopolymer with respect to the corresponding non-fluorinated material (from 11% to 48% for SA/CE vs SA/CE-FOX beads and from 11% to 94% for SA/CMC vs SA/CMC-FOX beads). Recyclability tests show good residual performance of SA/CMC-FOX beads after seven desorption/reuse cycles opening the way to more sustainable adsorbing processes for the removal of emerging pollutants from contaminated water.
In 2020, around 44% of natural gas in Germany was used in combined heat and power as well as in combined cycle gas turbines plants. As district heating will play an important role in future heating planning, the retrofit of these plants to hydrogen is a viable option. This paper analyzes a typical combined cycle power plant, including its balance of plant under consideration of different hydrogen blends. We show that retrofits are limited by the gas turbines in many cases. For instance, the preheater in the fuel gas system must be dimensioned higher than with natural gas. While pressure losses are very low, materials could be a problem due to higher volume flows. Additionally, the higher combustion temperatures in the gas turbine can compensate for possible efficiency losses in the Heat Recovery System Generator (HRSG) and steam turbine making this a suitable approach for electricity-led plants. However, for heat-led plants this leads to a reduction in the district heating output. Therefore, the performance of the HRSG must be considered as a limiting factor for heat-driven plants and the change in flue gas must be analyzed. Currently, hydrogen blends of 20–40 vol.-% appear feasible without major adjustments. The water content in the exhaust gas can also lead to problems in the HRSG and flue gas aftertreatment due to changes in the dew point. For higher hydrogen blends, a plant specific analysis is recommended.
Development of Open Educational Resources for Renewable Energy and the Energy Transition Process
(2021)
Amide synthases catalyze the formation of macrolactam rings from aniline-containing polyketide-derived seco-acids as found in the important class of ansamycin antibiotics. One of these amide synthases is the geldanamycin amide synthase GdmF, which we recombinantly expressed, purified and studied in detail both functionally as well as structurally. Here we show that purified GdmF catalyzes the amide formation using synthetically derived substrates. The atomic structures of the ligand-free enzyme and in complex with simplified substrates reveal distinct structural features of the substrate binding site and a putative role of the flexible interdomain region for the catalysis reaction.
To ensure reliable performance of Question Answering (QA) systems, evaluation of robustness is crucial. Common evaluation benchmarks commonly only include performance metrics, such as Exact Match (EM) and the F1 score. However, these benchmarks overlook critical factors for the deployment of QA systems. This oversight can result in systems vulnerable to minor perturbations in the input such as typographical errors. While several methods have been proposed to test the robustness of QA models, there has been minimal exploration of these approaches for languages other than English. This study focuses on the robustness evaluation of German language QA models, extending methodologies previously applied primarily to English. The objective is to nurture the development of robust models by defining an evaluation method specifically tailored to the German language. We assess the applicability of perturbations used in English QA models for German and perform a comprehensive experimental evaluation with eight models. The results show that all models are vulnerable to character-level perturbations. Additionally, the comparison of monolingual and multilingual models suggest that the former are less affected by character and word-level perturbations.
An intelligent battery management system (BMS) with end-edge-cloud connectivity – a perspective
(2025)
The widespread adoption of electric vehicles (EVs) and large-scale energy storage has necessitated advancements in battery management systems (BMSs) so that the complex dynamics of batteries under various operational conditions are optimised for their efficiency, safety, and reliability. This paper addresses the challenges and drawbacks of conventional BMS architectures and proposes an intelligent battery management system (IBMS). Leveraging cutting-edge technologies such as cloud computing, digital twin, blockchain, and internet-of-things (IoT), the proposed IBMS integrates complex sensing, advanced embedded systems, and robust communication protocols. The IBMS adopts a multilayer parallel computing architecture, incorporating end-edge-cloud platforms, each dedicated to specific vital functions. Furthermore, the scalable and commercially viable nature of the IBMS technology makes it a promising solution for ensuring the safety and reliability of lithium-ion batteries in EVs. This paper also identifies and discusses crucial challenges and complexities across technical, commercial, and social domains inherent in the transition to advanced end-edge-cloud-based technology.
This paper presents a new numerically efficient implementation of flow mixing algorithms in dynamic simulation of pipeline fluid transport. Mixed characteristics include molar mass, heat value, chemical composition and the temperature of the transported fluids. In the absence of chemical reactions, the modeling is based on the universal conservation laws for molar flows and total energy. The modeling formulates a sequence of linear systems, solved by a sparse linear solver, typically in one iteration per integration step. The functionality and stability of the developed simulation methods have been tested on a number of realistic network scenarios. The main output of the paper is a functioning and stable implementation of flow mixing algorithms for dynamic simulation of fluid transport networks.
Because of their resilience, Time-of-Flight (ToF) cameras are now essential components in scientific and industrial settings. This paper outlines the essential factors for modeling 3D ToF cameras, with specific emphasis on analyzing the phenomenon known as “wiggling”. Through our investigation, we demonstrate that wiggling not only causes systematic errors in distance measurements, but also introduces periodic fluctuations in statistical measurement uncertainty, which compounds the dependence on the signal-to-noise ratio (SNR). Armed with this knowledge, we developed a new 3D camera model, which we then made computationally tractable. To illustrate and evaluate the model, we compared measurement data with simulated data of the same scene. This allowed us to individually demonstrate various effects on the signal-to-noise ratio, reflectivity, and distance.
Visuelle Darstellungen von MINT-Berufen durch Bildgeneratoren: Wie viel Vielfalt ist möglich?
(2024)
In den vergangenen Jahren haben sich Text-zu-Bild-Transformer-Modelle wie DALL·E, Stable Diffusion und Midjourney etabliert, die realitätsnahe Bilder generieren. So wurden zwischen 2022 und 2023 über 15 Milliarden KI-Bilder produziert, Midjourney alleine zeigt eine Nutzendenbasis von 16 Millionen (Broz 2023; Valyaeva 2023; Zhou et al. 2024). Diese kritische retrospektive Analyse beschäftigt sich mit DALL·E Mini, einem der ersten öffentlich weit verbreiteten schwächeren Modelle, das für viele Nutzende den initialen Kontaktpunkt mit dieser Technologie darstellte.
Bei der Entwicklung von Kunststoffbauteilen kommen in kontinuierlich zunehmendem Maße Simulationen zum Einsatz. Vor dem Hintergrund von steigenden Produktanforderungen als auch dem unausweichlichen Zwang zur Schonung von Ressourcen ist der erweiterte Einsatz von Simulationswerkzeugen wichtiger Teil des Lösungsweges. Zu den nutzbaren, aber in Bezug zu Realprozessen bisher wenig eingesetzten Methoden gehört die Molekulardynamik Simulation. Auf Grundlage dieser Methode können auf mikroskopischer Ebene die tatsächlichen physikalischen Abläufe, die bei der Verarbeitung von Kunststoffen im Prozess auftreten, sichtbar gemacht werden. In dieser Arbeit wird beleuchtet, wie Randbedingungen in Anlehnung an den Extrusionsblasformprozess den Werkstoff Polyethylen auf mikroskopischer Ebene beeinflussen. Hierzu wird ein mesoskopisches Modell (Coarse-Graining) zur Beschreibung des Polymers genutzt. Dieses Modell wird durch die Bestimmung von Materialkennwerten verifiziert. Es wird der uniaxiale Zugversuch auf der Mikroskala modelliert, um Größen wie beispielsweise Elastizitätsmodul, Streckspannung oder Querkontraktionszahl zu ermitteln. Ebenso werden thermische Kenngrößen, insbesondere zur Charakterisierung des Kristallisationsverhaltens, bestimmt. Ziel dieser Untersuchungen ist, Effekte, die bei dynamisch ablaufenden Dehnungs- bzw. Kristallisationsvorgängen stattfinden, mikroskopisch zu beobachten und zu quantifizieren. Die ermittelten Kennwerte liegen insbesondere für die thermischen Größen in dichter Nähe zu experimentellen Daten. Das Spannungs-Dehnungs Verhalten wird qualitativ mit guter Übereinstimmung mit dem realen Verhalten wiedergegeben. Die kurze Zeitskala, auf der sich die Simulationsmodelle befinden, hat jedoch mikromechanisch extremeres Verhalten zur Folge, als makroskopisch beobachtet wird. Durch Erweiterung der Modelle werden biaxiale Verstreckvorgänge, wie sie im Extrusionsblasformprozess beispielsweise während des Aufblasens des Vorformlings auftreten, nachgebildet. Die Betrachtung verschiedener Abkühlbedingungen, insbesondere unter Formzwang, ist in Anlehnung an den Realprozess weiterer Schwerpunkt der Untersuchungen. Die Analyse der biaxial verstreckten Modelle offenbart, dass Entschlaufungsvorgänge während des Verstreckens die weitere Entwicklung der Polymersysteme dominieren. Es gelingt, die Dynamik von Kristallisationsvorgängen in Abhängigkeit von Verstreckgrad und Abkühlbedingungen durch unterschiedliche Größen (Verteilung von Verschlaufungspunkten, lokale Orientierungen) zu quantifizieren. Die erzielten Resultate zeigen auf, dass es mittels vergröberten Molekulardynamik Simulationen möglich ist, das mikromechanische Verständnis von Vorgängen, die bei der Verarbeitung von Kunststoffen auftreten, signifikant zu erweitern.
During the development phase of plastic components, simulations are being used to an increasing extent. Against the background of product requirements and the inevitable necessity of conserving resources, the expanded use of simulation tools is an essential part of the solution. Among available methods, but so far underutilized with respect to real-life processes, is the molecular dynamics simulation. By the use of this method it is possible to visualize the physical processes occurring on the microscopic level, as e.g. those that arise during plastics processing. This thesis examines how boundary conditions, which mimic the extrusion blow molding process, affect the behavior of polyethylene on the microscopic level. A mesoscopic model (coarse-graining) is applied to describe the polymer. Initially, this model is verified by determining material properties. The uniaxial tensile test is modeled on the micro-scale to identify parameters such as the elastic modulus, yield stress, and Poisson’s ratio. Additionally, thermal properties, particularly those characterizing the crystallization behavior, are identified. The objective of these investigations is the microscopic observation and quantification of effects that occur during dynamic stretching and crystallization processes. The calculated properties show good agreement with the experimental data, especially regarding the thermal parameters. Qualitatively, the stress-strain behavior is reproduced in alignment with experimentally observed results. However, the short time scale of the simulation models leads to micromechanical behavior that is more extreme than what is monitored on a macroscopic level. By extending the simulation models, biaxial stretching processes are simulated. These stretching processes resemble the situation during the inflation of the parison in the extrusion blow molding process. The examination of various cooling conditions, particularly by the use of mold constraints, is another focus of the investigations. The analysis of the biaxially stretched simulations reveals that disentanglement processes during stretching dominate the further development of polymer systems. It is possible to quantify the dynamics of crystallization processes depending on the degree of stretching and cooling conditions through various parameters (distribution of entanglement points, local orientations). The results indicate that coarse-grained molecular dynamics simulations are able to significantly enhance the micromechanical understanding of local events occurring during plastic processing.
Energy meteorology is an applied research field of meteorology that focuses on the study and prediction of weather conditions and events that affect energy production and use. This field has become increasingly important as the energy industry has become more dependent on weather conditions, especially in the areas of renewable energy sources such as wind energy, solar energy, and hydropower. The following paper has been written by experts of the Committee on Energy Meteorology of the German Meteorological Society summarizing their more than 30 years of experience and lessons learnt. It gives an overview of activities in energy meteorology that are already essential for the transformation of energy systems to systems with high shares of renewable energies. Building on this, the experts have created a vision of future topics that describe the future research landscape of energy meteorology. The authors explain that work in energy meteorology in recent years has primarily been concerned with the physically based modeling of wind and solar power generation and the development of short-term forecasting systems. In future years, a significant expansion of work in the areas of energy system modeling, digitalization, and climate change is expected. This includes the detailed consideration of regionally specified spatiotemporal variability for system design, the integration of artificial intelligence skills, the development of weather-related consumption based on smart meters, and the mapping of the effects of climate change on the energy system in planning and operating processes.
Optimal placement and upgrade of solar PV integration in a grid-connected solar photovoltaic system
(2024)
The shift towards renewable energy sources has heightened the interest in solar photovoltaic (SPV) systems, particularly in grid-connected configurations, to enhance energy security and reduce carbon emissions. Grid-tied SPVs face power quality challenges when specific grid codes are compromised. This study investigates and upgrades an integrated 90 kWp solar plant within a distribution network, leveraging data from Ghana's Energy Self-Sufficiency for Health Facilities (EnerSHelF) project. The research explores four scenarios for SPV placement optimization using dynamic programming and the Conditional New Adaptive Foraging Tree Squirrel Search Algorithm (CNAFTSSA). A Python-based simulation identifies three scenarios, high load nodes, voltage drop nodes, and system loss nodes, as the points for placing PV for better performance. The analysis revealed 85 %, 82.88 %, and 100 % optimal SPV penetration levels for placing the SPV at high load, voltage drop, and loss nodes. System active power losses were reduced by 72.97 %, 71.52 %, and 70.15 %, and reactive power losses by 73.12 %, 71.86 %, and 68.11 %, respectively, by placing the SPV at the above three categories of nodes. The fourth scenario applies to CNAFTSSA, achieving 100 % SPV penetration and reducing active and reactive power losses by 72.33 % and 72.55 %, respectively. This approach optimizes the voltage regulation (VR) from 24.92 % to 4.16 %, outperforming the VR of PV placement at high load nodes, voltage drop nodes, and loss nodes, where the voltage regulations are 5.25 %, 9.36 %, and 9.64 %, respectively. The novel CNAFTSSA for optimal SPV placement demonstrates its effectiveness in achieving higher penetration levels and improving system losses and VR. The findings highlight the effectiveness of strategic SPV placement and offer a comprehensive methodology that can be adapted for similar power distribution systems.
Lattice Boltzmann method (LBM) simulations of incompressible flows are nowadays common and well-established. However, for compressible turbulent flows with strong variable density and intrinsic compressibility effects, results are relatively scarce. Only recently, progress was made regarding compressible LBM, usually applied to simple one and two-dimensional test cases due to the increased computational expense. The recently developed semi-Lagrangian lattice Boltzmann method (SLLBM) is capable of simulating two- and three-dimensional viscous compressible flows. This paper presents bounce-back, thermal, inlet, and outlet boundary conditions new to the method and their application to problems including heated or cooled walls, often required for supersonic flow cases. Using these boundary conditions, the SLLBM's capabilities are demonstrated in various test cases, including a supersonic 2D NACA-0012 airfoil, flow around a 3D sphere, and, to the best of our knowledge, for the first time, the 3D simulation of a supersonic turbulent channel flow at a bulk Mach number of Ma=1.5 and a 3D temporal supersonic compressible mixing layer at convective Mach numbers ranging from Ma=0.3 to Ma=1.2. The results show that the compressible SLLBM is able to adequately capture intrinsic and variable density compressibility effects.
Design Strategies for an AC Loss Minimized Winding for a Fully Superconducting Wind Generator
(2025)
Pollution with anthropogenic waste, particularly persistent plastic, has now reached every remote corner of the world. The French Atlantic coast, given its extensive coastline, is particularly affected. To gain an overview of current plastic pollution, this study examined a stretch of 250 km along the Silver Coast of France. Sampling was conducted at a total of 14 beach sections, each with five sampling sites in a transect. At each collection site, a square of 0.25 m2 was marked. The top 5 cm of beach sediment was collected and sieved on-site using an analysis sieve (mesh size 1 mm), resulting in a total of approximately 0.8 m3 of sediment, corresponding to a total weight of 1300 kg of examined beach sediment. A total of 1972 plastic particles were extracted and analysed using infrared spectroscopy, corresponding to 1.5 particles kg−1 of beach sediment. Pellets (885 particles), polyethylene as the polymer type (1349 particles), and particles in the size range of microplastics (943 particles) were most frequently found. The significant pollution by pellets suggests that the spread of plastic waste is not primarily attributable to tourism (in February/March 2023). The substantial accumulation of meso- and macro-waste (with 863 and 166 particles) also indicates that research focusing on microplastics should be expanded to include these size categories, as microplastics can develop from them over time.
Experimental and Simulation based Analysis of an Active EMI Filter for automotive PFC Applications
(2024)
Aberrant Ras homologous (Rho) GTPase signalling is a major driver of cancer metastasis, and GTPase-activating proteins (GAPs), the negative regulators of RhoGTPases, are considered promising targets for suppressing metastasis, yet drug discovery efforts have remained elusive. Here, we report the identification and characterization of adhibin, a synthetic allosteric inhibitor of RhoGAP class-IX myosins that abrogates ATPase and motor function, suppressing RhoGTPase-mediated modes of cancer cell metastasis. In human and murine adenocarcinoma and melanoma cell models, including three-dimensional spheroid cultures, we reveal anti-migratory and anti-adhesive properties of adhibin that originate from local disturbances in RhoA/ROCK-regulated signalling, affecting actin-dynamics and actomyosin-based cell-contractility. Adhibin blocks membrane protrusion formation, disturbs remodelling of cell-matrix adhesions, affects contractile ring formation, and disrupts epithelial junction stability; processes severely impairing single/collective cell migration and cytokinesis. Combined with the non-toxic, non-pathological signatures of adhibin validated in organoids, mouse and Drosophila models, this mechanism of action provides the basis for developing anti-metastatic cancer therapies.
Grading student answers and providing feedback are essential yet time-consuming tasks for educators. Recent advancements in Large Language Models (LLMs), including ChatGPT, Llama, and Mistral, have paved the way for automated support in this domain. This paper investigates the efficacy of instruction-following LLMs in adhering to predefined rubrics for evaluating student answers and delivering meaningful feedback. Leveraging the Mohler dataset and a custom German dataset, we evaluate various models, from commercial ones like ChatGPT to smaller open-source options like Llama, Mistral, and Command R. Additionally, we explore the impact of temperature parameters and techniques such as few-shot prompting. Surprisingly, while few-shot prompting enhances grading accuracy closer to ground truth, it introduces model inconsistency. Furthermore, some models exhibit non-deterministic behavior even at near-zero temperature settings. Our findings highlight the importance of rubrics in enhancing the interpretability of model outputs and fostering consistency in grading practices.
Von der ersten Hausarbeit bis zum Examen: Wissenschaftliches Arbeiten ist eine Kernkompetenz in jedem Studium. Zum Erlernen der wichtigsten Methoden und Regeln des wissenschaftlichen Arbeitens geben Ihnen Martin Wördenweber und Paul R. Melcher einen prägnanten Leitfaden mit vielen Praxisbeispielen an die Hand. (Verlagsangaben)
Influence of Initialisation Parameter in Extended Kalman Filter on State of Charge Estimation
(2024)