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Approaches for the Use of Rapidly Growing Willow Rods in Earth-Based Construction
No full textAbstract This chapter investigates construction applications of natural fibres and
fast-growing plant-based materials, which can be processed into load-bearing components using specially tailored digital manufacturing technology. The sourcing and
production of the developed sustainable and recyclable freeform lightweight structures based on willow and earth are described in detail and material samples are
shown. At the end-of-life, the components can be disjoined and the composite materials in the components can be fully separated mechanically for composting, recycling
or incineration. Moreover, the developed structures were prototyped, implemented
and tested in full-scale research demonstrators at the 2023 German National Garden
Show in Mannheim (BUGA23). With this contribution we show the feasibility of
such novel robotic construction methods of biogenic materials in structural applications for the first time. The envisioned application potential lies particularly in the
new construction of residential buildings, hotels, office buildings and buildings with
similar uses
Passivity-Based Stabilisation and Coordination in Networked Multi-Energy Systems
No full textThis work proposes nonlinear controllers for achieving decentralised stabilisation and distributed coordination in networked multi-energy systems comprising gas and power networks. Asymptotic stability is ensured in each case using equilibrium-independent passivity theory. This approach yields nonlinear system stability results that depend only on the properties of the components, while being topology independent and allowing arbitrary network sizes and combinations
On the consistency of pseudo-potential lattice Boltzmann methods
No full textWe derive the partial differential equation (PDE) to which the pseudo-potential lattice Boltzmann method (P-LBM) converges under diffusive scaling, providing a rigorous basis for its consistency analysis. By establishing a direct link between the method\u27s parameters and physical properties—such as phase densities, interface thickness, and surface tension—we develop a framework that enables users to specify fluid properties directly in SI units, eliminating the need for empirical parameter tuning. This allows the simulation of problems with predefined physical properties, ensuring a direct and physically meaningful parametrization. The proposed approach is implemented in OpenLB, featuring a dedicated unit converter for multiphase problems. To validate the method, we perform benchmark tests—including planar interface, static droplet, Galilean invariance, and two-phase flow between parallel plates—using R134a as the working fluid, with all properties specified in physical units. The results demonstrate that the method achieves second-order convergence to the identified PDE, confirming its numerical consistency. These findings highlight the robustness and practicality of the P-LBM, paving the way for accurate and user-friendly simulations of complex multiphase systems with well-defined physical properties
Pulsed-laser induced gold microparticle fragmentation by thermal strain
Get PDFLaser fragmentation of suspended microparticles (MP-LFL) is an upcoming alternative to laser ab-
lation in liquid (LAL) that allows to streamline the delivery processes and to optimize the irradiation
conditions for best efficiency. Yet, the structural basis of this process is not well understood to date.
Herein we employed ultrafast x-ray scattering upon picosecond laser excitation of a gold microparticle
suspension in order to understand the thermal kinetics as well as structure evolution after fragmen-
tation. The experiments are complemented by simulations according to the two-temperature model
to verify the spatiotemporal temperature distribution. It is found that above a fluence threshold of
750 J/m2 the microparticles are fragmented within a nanosecond into several large pieces where the
driving force is the strain due to a strongly inhomogeneous heat distribution on the one hand and
stress confinement due to ultrafast heating compared to stress propagation on the other hand. An
additional limited formation of small clusters is attributed to photothermal decomposition on the
front side of the microparticles at a fluence of 2700 J/m2
Non-targeted analysis of lipophilic and hydrophilic metabolites to distinguish between fresh and frozen-thawed fish of certain fish species using comprehensive 1H NMR spectroscopy and multivariate data analysis
Get PDFFood fraud along the production chain is a well-known issue that requires an effective authenticity control. For the differentiation of fresh and frozen-thawed fish, H nuclear magnetic resonance (NMR) spectroscopy based methods in combination with multivariate data analysis have proven to be suitable in principle. Here, from a total of 317 samples (cod, rainbow trout, mackerel; fresh and frozen-thawed), the lipid and polar fractions of the fish flesh were analyzed, and classification models based on a principal components analysis with linear discriminant analysis (PCA-LDA) including cross-validation were generated. Additionally, data fusions were carried out. The obtained average accuracies of > 90% (94.0% based on the lipid fraction, 92.8% based on the polar fraction) and > 95% (95.6% based on a low-level data fusion, 95.5% based on a mid-level data fusion) demonstrated a promising differentiation. Further examinations confirmed that the non-targeted analysis appears to be mandatory as no marker substances were indicated in the loadings plots of the models. To evaluate whether the generated classification models are suitable to be used in a broader manner, they were applied to 13 fresh and 13 frozen-thawed samples from twelve other common edible fish species in a preliminary study. The classification model based on the low-level data fusion gave the best results (84.6% of all 26 samples correctly predicted). Thus, although these models are very suitable for analyzing cod, rainbow trout, or mackerel for a classification as fresh or frozen-thawed, they cannot generally be applied to samples of other fish species
Self-Supervised Learning Strategies for a Platform to Test the Toxicity of New Chemicals and Materials
Get PDFHigh-throughput toxicity testing offers a fast and cost-effective way to test large amounts of compounds. A key component for such systems is the automated evaluation via machine learning models. In this paper, we address critical challenges in this domain and demonstrate how representations learned via self-supervised learning can effectively identify toxicant-induced changes. We provide a proof-of-concept that utilizes the publicly available EmbryoNet dataset, which contains ten zebrafish embryo phenotypes elicited by various chemical compounds targeting different processes in early embryonic development. Our analysis shows that the learned representations using self-supervised learning are suitable for effectively distinguishing between the modes-of-action of different compounds. Finally, we discuss the integration of machine learning models in a physical toxicity testing device in the context of the TOXBOX project
The impact of accelerator data on our understanding of extensive air showers
Get PDFBefore the start of CERN in 1954, cosmic rays (CR) were the main source of high energy particles leading to the discovery of anti-matter, muons and first mesons. Then particle accelerators provided controlled environments for studying high-energy particle interactions, producing precise data leading to the development of the Standard Model. These data were used early-on to create models to study extensive air showers (EAS), complex particle cascades initiated by high-energy cosmic rays, but its only in the late 90\u27s that both EAS models and experiments became precise enough to start to see discrepancies between hadronic model predictions and EAS data. Problems like "the muon puzzle", characterized by an unexpected surplus of muons in cosmic ray showers, has propelled further investigations into particle interactions. This talk will discuss how accelerator data have improved the accuracy of EAS models, resolved discrepancies, and offered new insights into hadronic interactions. We will also address the challenges and future directions in integrating accelerator data with EAS observations, underscoring the importance of taking into account all type of colliding system. From the simplest electron-positron annihilation to the most complex Lead-Lead or the newest proton-Oxygen interactions, we need them all to resolve the remaining inconsistencies in the models and finally improve the predictions for Astroparticle physics
The Influence of Model Complexity on Demand Side Management with Distributed Energy Resources
Get PDFState-of-Grid-Based SoC Balancing and AC Coupling Control for DC Microgrids
Get PDFThe shift towards renewable energy has increased interest in dc microgrids as a promising integration solution. However, managing power-sharing, state-of-charge (SoC) balancing, and reliable ac coupling without communication remains challenging, necessitating advanced control methods. This article introduces a novel state-of-grid-based control for dc microgrids, enabling communication-free power-sharing and SoC balancing across battery systems, even with significant line resistances, while supporting synergistic ac coupling. Each battery locally projects its SoC into a small voltage offset around the nominal dc bus level; the resulting bus voltage therefore mirrors the average SoC of the entire microgrid. By using an analogous mapping for ac frequency, a single voltage–frequency metric unifies dc and ac domains. A small-signal Lyapunov analysis proves local exponential stability under realistic parameter spreads. The controller is implemented on a laboratory microgrid comprising two battery emulators, photovoltaic generation, programmable loads, and a Silicon Carbide interlink converter. Hardware results in islanded and grid-connected modes confirm voltage regulation, SoC equalization, and seamless power exchange
Metal–Insulator–Insulator–Metal (MIIM) Ag/SnO/AlO/Ag Diodes Fabricated by Ultraprecise Dispensing and Atomic Layer Deposition
Get PDFCurrently, high-frequency, ultra-fast, and tunneling diodes are mainly fabricated using traditional lithography and evaporation techniques, typically limited to wafer sizes. This work introduces a new method for fabricating metal–insulator–insulator–metal (MIIM) diodes using ultra-precise dispensing (UPD) printing techniques, providing a practical alternative to traditional lithography. Enabling highly precise material deposition, minimizing waste and boosting manufacturing efficiency. Both bottom and top electrodes of the MIIM diode are silver(Ag) and fabricated via UPD, while atomic layer deposition (ALD) is employed to deposit the insulating layers. 1 nm of tin oxide(SnO2) and 1 nm of aluminum oxide(AlO) sandwiched between the electrodes: The Ag/SnO/AlO/Ag MIIM diode has a contact area of ca. 5.4 µm × 4.0 µm determined by FIB-SEM. A quantum simulator based on the Wentzel-Kramers-Brillouin (WKB) method is used to analyze the diode\u27s performance and shows agreement with measurement results. The electrical characterization of the fabricated MIIM device exhibits a tunneling current in the nano- to microampere range, a zero-bias responsivity of −1.31 A/W, and dynamic resistance of 39.56 kΩ. Combining ultra-precise printing with innovative insulators provides a promising pathway to large-scale, low-cost production of high-performance MIM diodes for energy harvesting, high-frequency rectification, and flexible applications electronics