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We present a new concept of 3D polymer-based 1 × 4 beam splitter for wavelength splitting around 1550 nm. The beam splitter consists of IP-Dip polymer as a core and polydimethylsiloxane (PDMS) Sylgard 184 as a cladding. The splitter was designed and simulated with two different photonics tools and the results show high splitting ratio for single-mode and multi-mode operation with low losses. Based on the simulations, a 3D beam splitter was designed and realized using direct laser writing (DLW) process with adaptation to coupling to standard single-mode fiber. With respect to the technological limits, the multi-mode splitter having core of (4 × 4) μm 2 was designed and fabricated together with supporting stable mechanical construction. Splitting properties were investigated by intensity monitoring of splitter outputs using optical microscopy and near-field scanning optical microscopy. In the development phase, the optical performance of fabricated beam splitter was examined by splitting of short visible wavelengths using red light emitting diode. Finally, the splitting of 1550 nm laser light was studied in detail by near-field measurements and compared with the simulated results. The nearly single-mode operation was observed and the shape of propagating mode and mode field diameter was well recognized.
Power plant operators increasingly rely on predictive models to diagnose and monitor their systems. Data-driven prediction models are generally simple and can have high precision, making them superior to physics-based or knowledge-based models, especially for complex systems like thermal power plants. However, the accuracy of data-driven predictions depends on (1) the quality of the dataset, (2) a suitable selection of sensor signals, and (3) an appropriate selection of the training period. In some instances, redundancies and irrelevant sensors may even reduce the prediction quality.
We investigate ideal configurations for predicting the live steam production of a solid fuel-burning thermal power plant in the pulp and paper industry for different modes of operation. To this end, we benchmark four machine learning algorithms on two feature sets and two training sets to predict steam production. Our results indicate that with the best possible configuration, a coefficient of determination of R^2 = 0.95 and a mean absolute error of MAE=1.2 t/h with an average steam production of 35.1 t/h is reached. On average, using a dynamic dataset for training lowers MAE by 32% compared to a static dataset for training. A feature set based on expert knowledge lowers MAE by an additional 32 %, compared to a simple feature set representing the fuel inputs. We can conclude that based on the static training set and the basic feature set, machine learning algorithms can identify long-term changes. When using a dynamic dataset the performance parameters of thermal power plants are predicted with high accuracy and allow for detecting short-term problems.
A quantum-light source that delivers photons with a high brightness and a high degree of entanglement is fundamental for the development of efficient entanglement-based quantum-key distribution systems. Among all possible candidates, epitaxial quantum dots are currently emerging as one of the brightest sources of highly entangled photons. However, the optimization of both brightness and entanglement currently requires different technologies that are difficult to combine in a scalable manner. In this work, we overcome this challenge by developing a novel device consisting of a quantum dot embedded in a circular Bragg resonator, in turn, integrated onto a micromachined piezoelectric actuator. The resonator engineers the light-matter interaction to empower extraction efficiencies up to 0.69(4). Simultaneously, the actuator manipulates strain fields that tune the quantum dot for the generation of entangled photons with fidelities up to 0.96(1). This hybrid technology has the potential to overcome the limitations of the key rates that plague current approaches to entanglement-based quantum key distribution and entanglement-based quantum networks. Introduction
Demand-side management approaches that exploit the temporal flexibility of electric vehicles have attracted much attention in recent years due to the increasing market penetration. These demand-side management measures contribute to alleviating the burden on the power system, especially in distribution grids where bottlenecks are more prevalent. Electric vehicles can be defined as an attractive asset for distribution system operators, which have the potential to provide grid services if properly managed. In this thesis, first, a systematic investigation is conducted for two typically employed demand-side management methods reported in the literature: A voltage droop control-based approach and a market-driven approach. Then a control scheme of decentralized autonomous demand side management for electric vehicle charging scheduling which relies on a unidirectionally communicated grid-induced signal is proposed. In all the topics considered, the implications on the distribution grid operation are evaluated using a set of time series load flow simulations performed for representative Austrian distribution grids. Droop control mechanisms are discussed for electric vehicle charging control which requires no communication. The method provides an economically viable solution at all penetrations if electric vehicles charge at low nominal power rates. However, with the current market trends in residential charging equipment especially in the European context where most of the charging equipment is designed for 11 kW charging, the technical feasibility of the method, in the long run, is debatable. As electricity demand strongly correlates with energy prices, a linear optimization algorithm is proposed to minimize charging costs, which uses next-day market prices as the grid-induced incentive function under the assumption of perfect user predictions. The constraints on the state of charge guarantee the energy required for driving is delivered without failure. An average energy cost saving of 30% is realized at all penetrations. Nevertheless, the avalanche effect due to simultaneous charging during low price periods introduces new power peaks exceeding those of uncontrolled charging. This obstructs the grid-friendly integration of electric vehicles.
Aufgrund des anhaltenden Klimawandels wird eine Reduzierung des Ausstoßes von Kohlendioxid (CO2) immer wichtiger. Dabei könnte die Verwendung und Weiterentwicklung von CO2-Gashydraten als „Carbon Capture“-Methode (CC) einen großen Beitrag leisten. Bei der Bildung von Gashydraten wird Gas in den Hohlräumen eines Hydratgitters, welches sich unter hohen Drücken und niedrigen Temperaturen bildet, eingeschlossen. In dieser Arbeit wurden verfahrenstechnische Methoden getestet, um die Dynamik der Gashydratbildung zu verbessern. Dies wird vor allem durch eine Verbesserung von Massen- und Wärmetransport erreicht. Das Hauptproblem für die langen Bildungszeiten ist unter anderem eine sehr geringe Kontaktfläche. Diese kann durch den Einsatz eines Rührers, eines permanenten Gasdurchflusses und durch die Verwendung von Dry Water erhöht werden. Dry Water besteht aus vielen winzigen Wassertröpfchen, die von hydrophobiertem Siliziumdioxid (SiO2) umschlossen sind. Dadurch kann die Dauer der Induktions- und Wachstumsphase der Gashydratbildung verkürzt werden.
In dieser Arbeit wird ein Reaktor mit 30 ml Volumen und ein Schrägblattrührer mit einem Durchmesser von 17 mm verwendet. Der Rührer wurde bei einer maximalen Drehzahl von 15 000 rpm ca. alle 10 min für max. 30 s betrieben, bis die Wachstumsphase der Gashydratbildung beginnt. Der Gasdurchfluss hatte einen Volumenstrom von ca. 4 slm. Das verwendete Dry Water wurde in einem Haushaltsmixer bei 25 000 rpm aus Kieselsäure und Wasser hergestellt und besitzt einen Wasseranteil von 95 %. Es wurden unterschiedliche Kombinationen getestet. Die kürzeste Induktionszeit wurde mit durchschnittlich 6,9 min durch den Einsatz eines Rührers in Kombination mit einem Gasdurchfluss und Wasser erreicht, bei einem Volumenverhältnis zwischen Gas und Wasser von 8,8 v/v nach 60 min. Die größte Menge an Gashydrat konnte durch den Einsatz von Dry Water in Kombination mit einem Gasdurchfluss erreicht werden. Das führte nach 60 min zu einem Volumenverhältnis zwischen Gas und Wasser von 14,9 v/v. Die Induktionszeit betrug dabei 120 min.
The production of liquid-gas dispersions places high demands on the process technology, which requires knowledge of the bubble formation mechanisms, as well as the phase parameters of the media combinations used. To obtain the bubble sizes introduced to a flow not knowing the phase parameters, different process parameters are investigated. Their quality and applicability are evaluated. The results obtained make it possible to simplify long design processes of dispersion processes in manufacturing plants and to ensure the product quality of the products manufactured, by reducing waste.
In times of global climate change, it is increasingly important to investigate emissions and resource consumption of all machines and, if possible, to improve them. This includes within the transport sector car ferries.
In order to reduce the environmental impacts of car ferries, the electrification has penetrated into this sector, which has led to the world's first fully electric car ferry. One of the most important components to operate this ferry is the energy storage. Not only the battery storage of the ferry itself is needed, but also an onshore battery storage system is needed to support the electrical grid.
The present study examines how storage technologies and concepts can impact the environment considering the world's first all-electric car ferry, MF Ampere, which operates in Norway.
To examine this, the current onshore battery storage system is compared to a concrete sphere storage system. For this purpose, data from the first test run of this new storage technology, which was successfully carried out by the Fraunhofer Institute in 2016, is considered. Subsequently, a life cycle assessment of the two storage systems is carried out to compare the environmental impacts.
The concrete sphere storage system performs better for 15 of 17 impact categories compared to the existing onshore battery storage system. Depending on the impact category the impact reduction is about 2% to 8%.
Nevertheless, it is difficult to estimate how long the useful life and how good the efficiency of the concrete ball storage will be, since no system of this size has been tested yet. Also, the costs of the concrete sphere storage system have not been considered.
This paper presents a project developed at the K.S.Rangasamy College of Technology (Tamilnadu,India) aimed at designing, implementing, and testing an autonomous multipurpose vehicle with safe, efficient, and economic operation. This autonomous vehicle moves through the crop lines of a Agricultural land and performs tasks that are tedious and/or hazardous to the farmers. First, it has been equipped for spraying, but other configurations have also been designed, such as: a seeding ,plug platform to reach the top part of the plants to perform different tasks (pruning, harvesting, etc.), and a trailer to transport the fruits, plants, and crop waste.