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This study focuses on thermoelectric elements (TEE) as an alternative for room temperature control. TEE are semi-conductor devices that can provide heating and cooling via a heat pump effect without direct noise emissions and no refrigerant use. An efficiency evaluation of the optimal operating mode is carried out for different numbers of TEE, ambient temperatures, and heating loads. The influence of an additional heat recovery unit on system efficiency and an unevenly distributed heating demand are examined. The results show that TEE can provide heat at a coefficient of performance (COP) greater than one especially for small heating demands and high ambient temperatures. The efficiency increases with the number of elements in the system and is subject to economies of scale. The best COP exceeds six at optimal operating conditions. An additional heat recovery unit proves beneficial for low ambient temperatures and systems with few TEE. It makes COPs above one possible at ambient temperatures below 0 ∘C. The effect increases efficiency by maximal 0.81 (from 1.90 to 2.71) at ambient temperature 5 K below room temperature and heating demand Q˙h=100W but is subject to diseconomies of scale. Thermoelectric technology is a valuable option for electricity-based heat supply and can provide cooling and ventilation functions. A careful system design as well as an additional heat recovery unit significantly benefits the performance. This makes TEE superior to direct current heating systems and competitive to heat pumps for small scale applications with focus on avoiding noise and harmful refrigerants.
Koaxiale Stahlenergiepfähle
(2021)
Ein entscheidender Teil der Energiewende ist die Wärmewende im Gebäudesektor. Ein Schlüsselelement sind hier Wärmepumpen. Diese benötigen eine Wärmequelle, der sie Energie entziehen können, um sie auf ein höheres Temperaturniveau zu transformieren. Diese Wärmequelle kann bspw. das Erdreich sein, dessen Wärme durch Erdsonden erschlossen werden kann. In diesem Beitrag werden in Stahlpfähle integrierte Koaxialsonden mit dem Stand der Technik von Erdsonden gleichen Durchmessers bezüglich ihrer thermischen Leistungsmerkmale verglichen. Die Stahlenergiepfähle bieten neben der Wärmegewinnung weitere Vorteile, da sie auch eine statische Funktion übernehmen und rückstandsfrei zurückgebaut werden können. Es werden analytische und numerische Berechnungen vorgestellt, um die thermischen Potenziale beider Systeme zu vergleichen. Außerdem wird ein Testaufbau gezeigt, bei dem Stahlenergiepfähle in zwei verschiedenen Längen mit vorhandenen gängigen Erdsonden verglichen werden können. Die Berechnungen zeigen einen deutlichen thermischen Mehrertrag zwischen 26 % und 148 % der Stahlenergiepfähle gegenüber dem Stand der Technik abhängig vom Erdreich. Die Messergebnisse zeigen einen thermischen Mehrertrag von über 100 %. Es lässt sich also signifikante Erdsondenlänge einsparen. Dabei ist zu beachten, dass sich damit der thermisch genutzte Bereich des Erdreichs reduziert, wodurch die thermische Regeneration und/oder das Langzeitverhalten des Erdreichs an Bedeutung gewinnt.
Wind energy represents the dominant share of renewable energies. The rotor blades of a wind turbine are typically made from composite material, which withstands high forces during rotation. The huge dimensions of the rotor blades complicate the inspection processes in manufacturing. The automation of inspection processes has a great potential to increase the overall productivity and to create a consistent reliable database for each individual rotor blade. The focus of this paper is set on the process of rotor blade inspection automation by utilizing an autonomous mobile manipulator. The main innovations include a novel path planning strategy for zone-based navigation, which enables an intuitive right-hand or left-hand driving behavior in a shared human–robot workspace. In addition, we introduce a new method for surface orthogonal motion planning in connection with large-scale structures. An overall execution strategy controls the navigation and manipulation processes of the long-running inspection task. The implemented concepts are evaluated in simulation and applied in a real-use case including the tip of a rotor blade form.
In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.