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Assessing the impact of cyclical sequencing in make-to-stock production with sequence-dependent setup times
No full textCyclical sequencing is a well-known sequencing method in which the sequence of products is repeated cyclically. In make-to-stock production with sequence-dependent setup times, cyclical sequencing influences important logistical objectives in production as well as in the finished goods warehouse. We derive a simple model that enables companies to set the important parameters consistently. In simulation experiments, the model is validated for its influence on logistical objectives such as capacity requirements in production and mean finished goods inventories in the warehouse
Exact time-varying turnpikes for dynamic operation of district heating networks
No full textDistrict heating networks (DHNs) are crucial for decarbonizing the heating sector. Yet, their efficient and reliable operation requires the coordination of multiple heat producers and the consideration of future demands. Predictive and optimization-based control is commonly used to address this task, but existing results for DHNs do not account for time-varying problem aspects. Since the turnpike phenomenon can serve as a basis for model predictive control design and analysis, this paper examines its role in DHN optimization by analyzing the underlying optimal control problem with time-varying prices and demands. That is, we derive conditions for the existence of a unique time-varying singular arc, which constitutes the time varying turnpike, and we provide its closed-form expression. Additionally, we present converse turnpike results showing a exact time-varying case implies strict dissipativity of the optimal control problem. A numerical example illustrates our findings
Reducing the sampling complexity of energy estimation in quantum many-body systems using empirical variance information
No full textWe consider the problem of estimating the energy of a quantum state preparation for a given Hamiltonian in Pauli decomposition. For various quantum algorithms, in particular, in the context of quantum chemistry, it is crucial to have energy estimates with error bounds, as captured by guarantees on the problem's sampling complexity. In particular, when limited to Pauli basis measurements, the smallest sampling complexity guarantee comes from a simple single-shot estimator via a straightforward argument based on Hoeffding's inequality. In this work, we construct an adaptive estimator using the state's actual variance. Technically, our estimation method is based on the empirical Bernstein stopping (EBS) algorithm and grouping schemes, and we provide a rigorous tail bound, which leverages the state's empirical variance. In a numerical benchmark of estimating ground-state energies of several Hamiltonians, we demonstrate that EBS consistently improves upon elementary readout guarantees up to 1 order of magnitude
Current regulated LED driver with fast switch-off behavior for integrated optoelectronic implants
No full textThis paper presents a power, speed and area optimized mini-LED driver integrated in 180 nm CMOS HV process and capable of sinking up to 4mA current. It aims to cover various applications of optoelectronic implants with a single design. The current driver consists of a drain-regulated self-cascode current mirror. An additional carrier sweep-out circuit reduces the mini-LED voltage fall time down to 4.7 ns. The current can be digitally adjusted from 0.5 mA to 4 mA, depending on its use-case. The temperature-compensated current reference provides 0.01 mA with a temperature coefficient (TC) of 210 ppm/°C between -10 °C and 110 °C leading to a TC of 357 ppm/°C for the output current. The successfully performed measurements for circuit under pad constructs revealed a possible size reduction only limited by the dimensions of the on-die processed mini-LEDs of 0.1 mm x 0.2 mm
Stand und Perspektiven
No full textDie Herausgeber bieten eine Zusammenstellung der Nutzung des erneuerbaren Energieangebots in Deutschland im Jahr 2024. Zuerst ist die energiewirtschaftliche Gesamtsituation dargestellt und anschließend der aktuelle Stand der Nutzung der regenerativen Energien in Deutschland, einschließlich der wichtigsten Entwicklungen. Sowohl die Optionen zur primären Strom- und Wärmebereitstellung - einschließlich der Möglichkeiten einer KWK – als auch die Varianten einer Bereitstellung alternativer / erneuerbarer Kraftstoffe für den Verkehrssektor sind diskutiert. Darüber hinaus sind die Perspektiven marktbestimmender Technologien innerhalb der Sektoren Strom, Wärme und Mobilität analysiert. Das Buch bietet außerdem kurze Einblicke in das existierende und das potenziell zukünftige legale Regelwerk, die Integration der erneuerbaren Energien in unser Wirtschaftssystem sowie die sie determinierenden ökonomischen Rahmenbedingungen
Ausgewählte reaktionskinetische Daten von Gesteinskörnungen im alkalischen Milieu als mögliche Grundlagen zur Vorhersage der Alkaliempfindlichkeit
No full textEine Alkali-Kieselsäure-Reaktion (AKR) ist eine Reaktion, die innerhalb von Betonstrukturen stattfindet und deren Produkt durch Expansion die Dauerhaftigkeit der Strukturen signifikant verkürzen kann. Der zuverlässigste Schutz vor diesen Schäden ist die Vermeidung von alkalireaktiver Gesteinskörnung im Beton, für deren Detektion international ähnliche Prüfverfahren existieren. Diese basieren überwiegend auf der Bewertung der Dehnung von Probenkörpern, hergestellt mit der zu untersuchenden Gesteinskörnung. In dieser Arbeit wurde ein neues im Vorfeld entwickeltes Schnelltestverfahrens zur Detektion von alkalireaktiver Gesteinskörnung auf seine Eignung an unterschiedlichen Gesteinen geprüft. Dazu wurden die Gesteine präzise bezüglich seiner Zusammensetzung, Aufbau und Größe beschrieben und eine statistisch signifikante Anzahl an Versuchen durchgeführt. Durch die Modellierung der Versuchsergebnisse und die Ermittlung des Einflusses der Charakteristika der Gesteine auf diese Ergebnisse soll das Testverfahren auf Eignung geprüft und Bewertungskriterien für die Reaktivität der Gesteine entwickelt werden.An alkali-silica reaction (ASR) is a reaction that takes place within concrete structures and whose product can significantly shorten the durability of the structures through expansion. The most reliable protection against this damage is the avoidance of alkali-reactive aggregates in the concrete. There are similar international test methods for the detection of alkali-reactive aggregates. These are mainly based on the evaluation of the elongation of specimens made with the aggregate to be analysed. This study presents a potentially new test method based on solution tests and the investigations into its suitability. In this work, a new fast test procedure developed in advance for the detection of alkali-reactive aggregate was tested for its suitability on different aggregates. For this purpose, the aggregates were precisely described in terms of their composition, structure and size, and a statistically significant number of tests were carried out. By modeling the test results and determining the influence of the characteristics of the aggregates on these results, the test procedure can be tested for suitability and evaluation criteria for the reactivity of the aggregates can be developed
Incremental model order reduction of smoothed-particle hydrodynamic simulations
No full textEngineering simulations are usually based on complex, grid-based, or mesh-free methods for solving partial differential equations. The results of these methods cover large fields of physical quantities at very many discrete spatial locations and temporal points. Efficient compression methods can be helpful for processing and reusing such large amounts of data. A compression technique is attractive if it causes only a small additional effort and the loss of information with strong compression is low. The paper presents the development of an incremental singular value decomposition (SVD) strategy for compressing time-dependent particle simulation results. The approach is based on an algorithm that was previously developed for grid-based, regular snapshot data matrices. It is further developed here to process highly irregular data matrices generated by particle simulation methods during simulation. Various aspects important for information loss, computational effort, and storage requirements are discussed, and corresponding solution techniques are investigated. These include the development of an adaptive rank truncation approach, the assessment of imputation strategies to close snapshot matrix gaps caused by temporarily inactive particles, a suggestion for sequencing the data history into temporal windows as well as bundling the SVD updates. The simulation-accompanying method is embedded in a parallel, industrialized smoothed-particle hydrodynamics software and applied to several 2D and 3D test cases. The proposed approach reduces the memory requirement by about 90% and increases the computational effort by about 10%, while preserving the required accuracy. For the final application of a water turbine, the temporal evolution of the force and torque values for the compressed and simulated data is in excellent agreement
Elemental analysis of catalysts in a fixed-bed reactor by laser-induced breakdown spectroscopy
No full textIn situ and operando characterization techniques provide molecular, surface, electronic, and structural information about catalysts in reactors. Accessing changes in the “Elemental Composition” of catalysts under reaction conditions remains yet an unsolved challenge. This work takes a first step in this direction by investigating the potential and limitations of Laser-Induced Breakdown Spectroscopy (LIBS) for monitoring catalyst deactivation, specifically during Dry Reforming of Methane over Ni/γ-Al2O3 and Propane Dehydrogenation over VOX/γ-Al2O3. The study increases complexity stepwise, starting from spatially resolved ex situ LIBS analysis of catalyst particles removed from the reactor after reaction, to the development of a fiber-optic LIBS system and time-resolved intra-reactor measurements. Spectroscopic measurements are complemented by monitoring catalyst activity and temperature at each stage. For the dry reforming of methane, a correlation was found between catalyst performance, temperature, and the spatial distribution of coke, with distinct zoning along the catalyst bed. A cost-effective fiber-optic LIBS probe was developed, allowing for intra-reactor measurements under harsh conditions. The LIBS probe was used to study catalyst deactivation and regeneration during propane dehydrogenation by measuring time-resolved carbon and vanadium profiles. A Partial Least Squares regression model was developed to correlate the amount of carbon deposits with the LIBS spectra, allowing the derivation of quantitative statements. Despite its limitations, such as invasiveness and gas sensitivity, the study highlights the potential of LIBS as an in situ/operando elemental analysis technique
Learned discrepancy reconstruction and benchmark dataset for magnetic particle imaging
No full textMagnetic Particle Imaging (MPI) is an emerging imaging modality based on the magnetic response of superparamagnetic iron oxide nanoparticles to achieve high-resolution and real-time imaging without harmful radiation. One key challenge in the MPI image reconstruction task arises from its underlying noise model, which does not fulfill the implicit Gaussian assumptions that are made when applying traditional reconstruction approaches. To address this challenge, we introduce the Learned Discrepancy Approach, a novel learning-based reconstruction method for inverse problems that includes a learned discrepancy function. It enhances traditional techniques by incorporating an invertible neural network to explicitly model problem-specific noise distributions. This approach does not rely on implicit Gaussian noise assumptions, making it especially suited to handle the sophisticated noise model in MPI and also applicable to other inverse problems. To further advance MPI reconstruction techniques, we introduce the MPI-MNIST dataset — a large collection of simulated MPI measurements derived from the MNIST dataset of handwritten digits. The dataset includes noise-perturbed measurements generated from state-of-the-art model-based system matrices and measurements of a preclinical MPI scanner device. This provides a realistic and flexible environment for algorithm testing. Validated against the MPI-MNIST dataset, our method demonstrates significant improvements in reconstruction quality in terms of structural similarity, achieving up to 7.9% higher SSIM as well as 2.2 dB higher PSNR compared to classical reconstruction techniques across varying noise levels, underscoring its robustness in high-noise scenarios