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As part of the transnational research project EDITOR, a parabolic trough collector system (PTC) with concrete thermal energy storage (C-TES) was installed and commissioned in Limassol, Cyprus. The system is located on the premises of the beverage manufacturer KEAN Soft Drinks Ltd. and its function is to supply process steam for the factory's pasteurisation process [1]. Depending on the factory's seasonally varying capacity for beverage production, the solar system delivers between 5 and 25 % of the total steam demand. In combination with the C-TES, the solar plant can supply process steam on demand before sunrise or after sunset. Furthermore, the C-TES compensates the PTC during the day in fluctuating weather conditions. The parabolic trough collector as well as the control and oil handling unit is designed and manufactured by Protarget AG, Germany. The C-TES is designed and produced by CADE Soluciones de Ingeniería, S.L., Spain. In the focus of this paper is the description of the operational experience with the PTC, C-TES and boiler during the commissioning and operation phase. Additionally, innovative optimisation measures are presented.
In this work, three patent pending calibration methods for heliostat fields of central receiver systems (CRS) developed by the Solar-Institut Jülich (SIJ) of the FH Aachen University of Applied Sciences are presented. The calibration methods can either operate in a combined mode or in stand-alone mode. The first calibration method, method A, foresees that a camera matrix is placed into the receiver plane where it is subjected to concentrated solar irradiance during a measurement process. The second calibration method, method B, uses an unmanned aerial vehicle (UAV) such as a quadrocopter to automatically fly into the reflected solar irradiance cross-section of one or more heliostats (two variants of method B were tested). The third calibration method, method C, foresees a stereo central camera or multiple stereo cameras installed e.g. on the solar tower whereby the orientations of the heliostats are calculated from the location detection of spherical red markers attached to the heliostats. The most accurate method is method A which has a mean accuracy of 0.17 mrad. The mean accuracy of method B variant 1 is 1.36 mrad and of variant 2 is 1.73 mrad. Method C has a mean accuracy of 15.07 mrad. For method B there is great potential regarding improving the measurement accuracy. For method C the collected data was not sufficient for determining whether or not there is potential for improving the accuracy.
Solar-electric propulsion (SEP) is superior with
respect to payload capacity, flight time and
flexible launch window to the conventional
interplanetary transfer method using chemical
propulsion combined with gravity assists. This fact
results from the large exhaust velocities of electric
low–thrust propulsion and is favourable also for
missions to the giant planets, Kuiper-belt objects
and even for a heliopause probe (IHP) as shown in
three studies by the authors funded by DLR. They
dealt with a lander for Europa and a sample return
mission from a mainbelt asteroid [1], with the
TANDEM mission [2]; the third recent one
investigates electric propulsion for the transfer to
the edge of the solar system.
All studies are based on triple-junction solar arrays,
on rf-ion thrusters of the qualified RIT-22 type and
they use the intelligent trajectory optimization
program InTrance [3].
Zur Bedeutung der Workload
(2005)
The impact of wake model effects is investigated for two highly
non-planar lifting systems. Dependent on the geometrical
arrangement of the configuration, the wake model shape is found
to considerably affect the estimation. Particularly at higher angles
of attack, an accurate estimation based on the common linear wake
model approaches is involved.
Euler-based induced drag estimation for highly non-planar lifting systems during conceptional design
(2013)
The potential of SMART climbing robot combined with a weatherproof cabin for rotor blade maintenance
(2016)
This paper presents the laser-based powder bed fusion (L-PBF) using various glass powders (borosilicate and quartz glass). Compared to metals, these require adapted process strategies. First, the glass powders were characterized with regard to their material properties and their processability in the powder bed. This was followed by investigations of the melting behavior of the glass powders with different laser wavelengths (10.6 µm, 1070 nm). In particular, the experimental setup of a CO2 laser was adapted for the processing of glass powder. An experimental setup with integrated coaxial temperature measurement/control and an inductively heatable build platform was created. This allowed the L-PBF process to be carried out at the transformation temperature of the glasses. Furthermore, the component’s material quality was analyzed on three-dimensional test specimen with regard to porosity, roughness, density and geometrical accuracy in order to evaluate the developed L-PBF parameters and to open up possible applications.
Often, research results from collaboration projects are not transferred into productive environments even though approaches are proven to work in demonstration prototypes. These demonstration prototypes are usually too fragile and error-prone to be transferred
easily into productive environments. A lot of additional work is required.
Inspired by the idea of an incremental delivery process, we introduce an architecture pattern, which combines the approach of Metrics Driven Research Collaboration with microservices for the ease of integration. It enables keeping track of project goals over the course of the collaboration while every party may focus on their expert skills: researchers may focus on complex algorithms,
practitioners may focus on their business goals.
Through the simplified integration (intermediate) research results can be introduced into a productive environment which enables
getting an early user feedback and allows for the early evaluation of different approaches. The practitioners’ business model benefits throughout the full project duration.
The integration of product data from heterogeneous sources and manufacturers into a single catalog is often still a laborious, manual task. Especially small- and medium-sized enterprises face the challenge of timely integrating the data their business relies on to have an up-to-date product catalog, due to format specifications, low quality of data and the requirement of expert knowledge. Additionally, modern approaches to simplify catalog integration demand experience in machine learning, word vectorization, or semantic similarity that such enterprises do not have. Furthermore, most approaches struggle with low-quality data. We propose Attribute Label Ranking (ALR), an easy to understand and simple to adapt learning approach. ALR leverages a model trained on real-world integration data to identify the best possible schema mapping of previously unknown, proprietary, tabular format into a standardized catalog schema. Our approach predicts multiple labels for every attribute of an inpu t column. The whole column is taken into consideration to rank among these labels. We evaluate ALR regarding the correctness of predictions and compare the results on real-world data to state-of-the-art approaches. Additionally, we report findings during experiments and limitations of our approach.
The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.
Seismic design of buried pipeline systems for energy and water supply is not only important for plant and operational safety but also for the maintenance of the supply infrastructure after an earthquake. The present paper shows special issues of the seismic wave impacts on buried pipelines, describes calculation methods, proposes approaches and gives calculation examples. This paper regards the effects of transient displacement differences and resulting tensions within the pipeline due to the wave propagation of the earthquake. However, the presented model can also be used to calculate fault rupture induced displacements. Based on a three-dimensional Finite Element Model parameter studies are performed to show the influence of several parameters such as incoming wave angle, wave velocity, backfill height and synthetic displacement time histories. The interaction between the pipeline and the surrounding soil is modeled with non-linear soil springs and the propagating wave is simulated affecting the pipeline punctually, independently in time and space. Special attention is given to long-distance heat pipeline systems. Here, in regular distances expansion bends are arranged to ensure movements of the pipeline due to high temperature. Such expansion bends are usually designed with small bending radii, which during the earthquake lead to high bending stresses in the cross-section of the pipeline. Finally, an interpretation of the results and recommendations are given for the most critical parameters.
Pulmonary arterial cannulation is a common and effective method for percutaneous mechanical circulatory support for concurrent right heart and respiratory failure [1]. However, limited data exists to what effect the positioning of the cannula has on the oxygen perfusion throughout the pulmonary artery (PA). This study aims to evaluate, using computational fluid dynamics (CFD), the effect of different cannula positions in the PA with respect to the oxygenation of the different branching vessels in order for an optimal cannula position to be determined. The four chosen different positions (see Fig. 1) of the cannulas are, in the lower part of the main pulmonary artery (MPA), in the MPA at the junction between the right pulmonary artery (RPA) and the left pulmonary artery (LPA), in the RPA at the first branch of the RPA and in the LPA at the first branch of the LPA.
Primäre Ziele des Internets der Dinge sind die Steuerung physischer Gegenstände aus der Distanz und das Erfassen von Informationen aus dem Umfeld dieser Gegenstände. Dazu werden Hardwarekomponenten in Gegenstände des täglichen Lebens und die Umwelt integriert. Mithilfe von Informations- und Kommunikationstechnologien entsteht hieraus das Internet der Dinge (Internet of Things, IoT). Vor einem Jahr wurde mit Narrowband Internet of Things (NB-IoT) eine Technologie die es ermöglicht, Hardwarekomponenten energieeffizient und unmittelbar über das Mobilfunknetz zu vernetzen. Gegenstände werden dadurch über große Reichweiten eigenständig kommunikationsfähig. Das IoT steht mit NB-IoT vor einem gestiegenen Nutzenpotenzial, da eine zunehmende Anzahl miteinander verbundener Gegenstände und der Austausch größerer Datenmengen realisierbar sind. Damit sind aus wirtschaftlicher Sicht neue, innovative Anwendungsfälle des IoT möglich, die auch bereits in der Praxis diskutiert werden. In diesem Beitrag wird anhand eines konkreten Anwendungsfalls untersucht, welche neuen Geschäfts- bzw. Partnermodelle durch die gemeinsame Nutzung von NB-IoT-Daten und Big Data-Technologien entstehen und welcher qualitative Mehrwert für die an einem Anwendungsfall beteiligten Stakeholder geschaffen wird. Dazu wird – einem konstruktionsorientierten Forschungsansatz folgend – ein Bewertungsrahmen zur qualitativen Wertschöpfungsanalyse von NB-IoT entwickelt, der u.a. auf der Schablone nach Cockburn und dem Business Model Canvas basiert. Anhand dieses Bewertungsrahmens wird ein Anwendungsfall untersucht, der in anonymisierter Form an konkrete Praxisprojekte angelehnt ist. Konkret wird ein Anwendungsfall betrachtet, der einen Fahrradverleih 2.0 basierend auf dem Einsatz von NB-IoT vorschlägt. Aus den Untersuchungsergebnissen gehen beispielsweise Erkenntnisse hervor, wie Geschäftsmodelle auf
Momentan finden in vielen Branchen umfassende Veränderungen von Märkten und Wertschöpfungsketten statt, welche auch als Digitale Transformation bezeichnet werden. In diesem Zusammenhang wird das Internet der Dinge (Internet of Things, IoT) als ein wichtiger technischer Enabler der Veränderungen angesehen. Primäre Ziele des IoT sind die Steuerung physischer Gegen-stände aus der Distanz und das Erfassen von Informationen aus dem Umfeld dieser Gegenstände. Welche neuen Geschäfts-bzw. Partnermodelle entstehen durch die gemeinsame Nutzung von IoT-Daten und Big-Data-Technologien und welcher qualitative Mehrwert wird dadurch geschaffen? Als Antwort wird in diesem Beitrag ein Bewertungsrahmen zur qualitativen Wertschöpfungsanalyse von IoT vorgeschlagen. Anhand dieses Bewertungsrahmens wird ein Anwendungsfall untersucht, der in anonymisierter Form an konkrete Praxisprojekte angelehnt ist. Konkret wird ein Anwendungsfall betrachtet, der eine Abfallwirtschaft 2.0 basierend auf dem Einsatz von IoT vorschlägt. Aus den Untersuchungsergebnissen gehen beispielsweise Erkenntnisse hervor, wie Geschäftsmodelle auf Basis eines unentgeltlichen Informationsaustauschs durch IoT gestaltet werden können.