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Denoising the system matrix with deep neural networks for better MPI reconstructions
No full textMagnetic Particle Imaging commonly relies on the system matrix (SM) to reconstruct particle distributions, but noise during acquisition limits both its resolution and image quality. Traditionally, noise reduction requires averaging multiple measurements, which increases acquisition time. This paper presents a deep neural network trained on simulated SMs and measured background noise, which effectively generalizes to real-world data. The model recovers higher frequency components of the SM and serves as a general pre-processing step, enhancing image reconstruction quality while reducing the need for extensive averaging, thus accelerating SM acquisition
Dynamic deep learning based super-resolution for the shallow water equations
No full textCorrectly capturing the transition to turbulence in a barotropic instability requires fine spatial resolution. To reduce computational cost, we propose a dynamic super-resolution approach where a transient simulation on a coarse mesh is frequently corrected using a U-net-type neural network. For the nonlinear shallow water equations, we demonstrate that a simulation with the Icosahedral Nonhydrostatic ocean model with a 20 km resolution plus dynamic super-resolution trained on a 2.5km resolution achieves discretization errors comparable to a simulation with 10 km resolution. The neural network, originally developed for image-based super-resolution in post-processing, is trained to compute the difference between solutions on both meshes and is used to correct the coarse mesh solution every 12 h. We show that the ML-corrected coarse solution correctly maintains a balanced flow and captures the transition to turbulence in line with the higher resolution simulation. After an 8 d simulation, the L2-error of the corrected run is similar to a simulation run on a finer mesh. While mass is conserved in the corrected runs, we observe some spurious generation of kinetic energy
Aktive Planung von robusten gekoppelten Netzen
No full textDurch die zunehmende Elektrifizierung in dem Wärmesektor ergeben sich neue Herausforderungen wie z. B. stark gestiegene Lasten und die damit verbundenen Anforderungen an einen schnellen Netzausbau, aber auch Chancen zur Nutzung von Flexibilitäten im Netzbetrieb. Dieser Beitrag stellt eine Methode zur effizienten Planung von mit Wärmenetzen gekoppelten Stromnetzen vor. Zunächst wird das Flexibilitätspotential steuerbarer Verbraucher für die Netzplanung genutzt, um Netzengpässe zu vermeiden und kostenintensive Netzausbauten zu verzögern. Des Weiteren wird ein neuartiger Ansatz zur verteilungsrobusten Optimierung des Betriebs gekoppelter Strom- und Wärmenetze unter Unsicherheiten vorgestellt. Dieser Ansatz basiert auf einem verteilungsrobusten zufallsbeschränkten Leistungsflussmodell (DRCC-OPF), das Unsicherheiten in wetterabhängigen Lastprognosen berücksichtigt. Das Gesamtkonzept integriert beide Ansätze in ein iteratives Planungsverfahren, das die Flexibilität des Netzbetriebs optimiert und gleichzeitig die Robustheit des Zielnetzes langfristig sicherstellt. Das Konzept ist Teil der gesamten integrierten Netzplanungsmethode, die im Rahmen des Teilvorhabens iNeP entwickelt wird.Bundesministerium für Wirtschaft und Klimaschutz (BMWK
Influence of grid aggregation on short circuit properties
No full textThe shift from conventional towards renewable energies causes a transition from centralized to decentralized generation. Decentralized energy resources lead to challenges but also chances for the operation of future power grids. Therefore, stability and protection phenomena are addressed. Different simulations need to be performed to prove the suitability of future power grids. These simulations include dynamic simulations with high numerical effort. For many applications, this numerical complexity needs to be reduced to ensure real-time capability or to perform simulations for many different systems and scenarios. A method for reducing the numerical complexity is grid aggregation. However, grid aggregation implies an error to the investigations. This error needs to be estimated. In this paper, it is demonstrated that the influence of grid aggregation on the grid's short circuit properties is small enough to use grid aggregation for grid protection studies. The paper's investigations were motivated by similar findings about the influence of grid aggregation on voltage stability properties.Bundesministerium für Wirtschaft und Klimaschutz (BMWK
Synthesis of Pt Carbon Aerogel Electrocatalysts with Multiscale Porosity Derived from Cellulose and Chitosan Biopolymer Aerogels via Supercritical Deposition for Hydrogen Evolution Reaction
No full textThe aim of this study is to investigate the activity and stability of carbon aerogel‐supported platinum electrocatalysts in the hydrogen evolution reaction, compared to current solutions based on carbon black. Self‐synthesized carbon aerogels (pyrolyzed cellulose, and chitosan‐based aerogels) with multiscale porosity and high overall specific surface area (up to ≈2500 m² g⁻¹), as well as Vulcan XC‐72R supports were loaded via supercritical deposition (SCD) with platinum nanoparticles (mean particle diameter ≈1.3–2.0 nm, 2.8–3.8 wt% Pt loading). Overpotentials ranged from 46.5 to 50.0 mV at 10 mA cm⁻², whereas self‐synthesized electrocatalysts had similar overpotentials as compared to a commercial catalyst with ≈8–10 times higher Pt loading. In addition, Pt‐carbon aerogel electrocatalysts had higher stability and durability as compared to Pt‐Vulcan, most probably due to the high micro‐ to mesoporosity of carbon aerogels, which promotes nanoparticle stability. The current density at 40 mV for Pt‐Vulcan decreased by 80% after 20 h, whereas an insignificant drop was observed for Pt‐carbon aerogels. These results show that the applied combination of materials (biopolymer‐based carbon aerogels) and loading method (SCD) are a promising approach for synthesizing stable electrocatalysts with reduced platinum content for green hydrogen production
Performing Polarized Raman and Digital Image Correlation Analysis to Understand the Increased Ductility of Microscale Epoxy Materials
No full textThe highly cross‐linked (epoxy) matrix material in fiber reinforced polymers has a microscopic volume between fibers and therefore exhibits different mechanical behavior in comparison to standard bulk epoxy samples. It has been found in previous studies that a decreased epoxy gauge volume leads to an increased deformation ability (necking and shear band formation). By using laser cutting to create dogbone samples from manufactured epoxy films, the gauge volume can be further reduced in comparison to previous studies, and the ductility can be enhanced even further. To understand load‐induced molecular mechanisms responsible for the increase in ductility at macroscale, this study combines digital image correlation (DIC) with tensile tests and precise force measurement. The global and local strains are calculated using the DIC data. The determined strains reach values up to 80% (global strains) and 120% (local strains), respectively. These strain values are significantly higher than those of archetypical brittle epoxy bulk samples (less than 10%). Polarized Raman spectra show that load‐bearing backbone molecules in the deformed film sample regions are oriented in the tensile load direction. This orientation might be due to the unraveling of entanglements, which can be seen as a sudden decrease followed by a subsequent rise in engineering stress values during deformation
Comparison between TiN coating on porous Ti-6Al-4V produced by PBF-EB/M or PM for bipolar plates in PEM fuel cells
No full textThe exceptional corrosion resistance, low weight, and high strength of titanium (Ti) make it an excellent choice for components in proton exchange membrane fuel cells (PEMFC). However, during PEMFC operation, Ti undergoes passivation, which diminishes the bipolar plates' (BP) ability to transport electrons between cells. Applying titanium nitride (TiN) coatings, known for their good conductive properties, can resolve this issue and enhance corrosion resistance. Additionally, using additive manufacturing (AM) to produce BP offers numerous benefits in terms of structural control for more intricate designs. This study examines the impact of TiN coating via gas nitriding on Ti6Al-4V open structures created by powder bed fusion-electron beam/metal (PBF-EB/M) or PM routes, focusing on the surface characteristics such as composition and interfacial contact resistance (ICR)
Qualitatively elucidating the molecular characteristics of precursors for saturated halogenated disinfection byproducts in chlorinated urban eutrophic lake water by ultrahigh-resolution mass spectrometry
No full textLake eutrophication affects the molecular composition of aquatic dissolved organic matter (DOM) and halogenated disinfection byproducts (Xn-DBPs). However, the effects of autochthonous DOM on the Xn-DBPs formation during disinfection of natural eutrophic water from the perspective of biological metabolism are still poorly revealed. Herein, the natural urban eutrophic lake (UEL) water with slight eutrophication was employed to elucidate the discrepancies in Xn-DBPs formation between autochthonous and allochthonous DOM based on the ultrahigh-resolution mass spectrometry. The number and its proportion of nitrogenous Xn-DBPs in chlorinated UEL water samples were significantly larger (p < 0.05) than those for chlorinated SRNOM. Microbes dominated by Microcystis contributed largely to releasing autochthonous DOM for Xn-DBPs formation upon disinfection. The Xn-DBPs species mainly derived from microorganisms were highly saturated, reduced, bioavailable, nitrogenous, and toxic but lowly oxidized and aromatic than terrestrially derived Xn-DBPs species. Moreover, for the first time, the connection between microbial lipid metabolism and Xn-DBPs species exclusively identified in chlorinated UEL water indicated the considerable contribution of lipid metabolites to saturated Xn-DBPs species. The specific biochemical mechanism of Xn-DBPs formation from autochthonous DOM caused by the lysis of microbe cells highlighted the significant contribution of microbial metabolic activities, particularly lipid metabolism, to the generation of highly saturated and nitrogenous Xn-DBPs during chlorination. This study has also reported a novel data interpretation paradigm for Xn-DBPs research, deepening our understanding towards the formation mechanisms of microbe-derived Xn-DBPs species from the view of microbial metabolic pathways
Large components in inhomogeneous random graphs
No full textThis thesis studies two types of inhomogeneous random graphs, so-called rank-1 models and the weighted random connection model. Under suitable parameter choices, both random graphs exhibit a scale-free degree distribution as observed in real-world complex networks. For both models, we study the sizes of large components in the subcritical regime. In the rank-1 case, we also establish quantitative Poisson approximation results for cycle counts. The latter allow us to deduce the asymptotic distributions of the lengths of the shortest and of the longest cycle in the subcritical regime.Diese Arbeit befasst sich mit zwei Arten von inhomogenen Zufallsgraphen, sogenannten Rang-1-Modellen und dem gewichteten Random Connection Model. Mit geeigneten Parametern weisen beide Modelle skalenfreie Gradverteilungen auf, wie sie empirisch bei komplexen Netzwerken zu beobachten sind. Wir untersuchen die Größen der großen Komponenten beider Modelle im subkritischen Regime. Für die Rang-1-Modelle zeigen wir außerdem quantitative Resultate hinsichtlich der Poisson-Approximation der Anzahl an Kreisen. Damit schließen wir auf die asymptotischen Verteilungen der Längen des kürzesten und des längsten Kreises im subkritischen Regime
Real-Time Flexibility Allocation among Distributed Energy Resources: A Digital Twin-Driven Dynamic Optimization Approach
No full textThe transition towards decentralized energy systems, driven by the integration of distributed energy resources (DERs), presents significant challenges for grid flexibility and operational planning. In this paper, a novel digital twin-driven optimization methodology is introduced for real-time flexibility provision in decentralized energy cells. By using dynamic simulation models, the proposed approach disaggregates centralized load requests into component-specific signals tailored to Electric Heat Pumps (EHPs) and Battery Electric Storage Systems (BESSs). A two-stage framework
is proposed: a preparation phase, where dynamic system behaviors are analyzed and linearized to create simplified gray-box models for optimization, and a provision phase, where optimized control signals are generated to meet realtime flexibility demands. A case study demonstrates the effectiveness of the methodology, highlighting the capability to manage diverse DERs under varying conditions. The results underscore the potential of digital twin-driven frameworks to enhance grid stability and support the evolving needs of decentralized energy systems