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Die autonome, unbemannte Luftfahrt ist einer der Schlüsselsektoren für die Zukunft der Luftfahrt. In diesem rasant wachsenden Bereich nehmen senkrecht startende und senkrecht landende Flugzeuge (Vertical Take-Off and Landing – VTOL) einen besonderen Platz ein. Ein VTOL-Flugzeug (manchmal auch „Transitionsfluggerät“ genannt) verbindet die Eigenschaft des Helikopters, überall starten und landen zu können, mit den Geschwindigkeits-, Reichweiten und Flugdauervorteilen des Starrflüglers. Grundsätzlich wird die Senkrechtstart- und -landefähigkeit sowohl von zivilen als auch von militärischen Betreibern unbemannter Fluggeräte (UAVs) gewünscht. Trotzdem bietet der Markt nur eine geringe Anzahl von VTOL-UAVs, da qualitativ hochwertige Entwürfe eine ausgesprochene Herausforderung in der Entwicklung darstellen. An der FH Aachen wird deshalb seit über 5 Jahren an der Auslegung und Analyse von solchen unbemannten VTOL Flugzeugen geforscht. Das neuste Projekt ist der Eigenentwurf einer großen, senkrechtstartenden Transportdrohne. Das „PhoenAIX“ getaufte Fluggerät wird von Falk Götten und Felix Finger im Rahmen einer EFRE-Förderung entwickelt.
Wie sieht das unbemannte Flugzeug von Übermorgen aus? Dieser Frage stellen sich Forscher an der Fachhochschule Aachen. Die weltweit rasant fortschreitende Entwicklung des Marktes für unbemannte Fluggeräte (UAVs - „Unmanned Aerial Vehicles“) bietet großes Potenzial für Wachstum und Wertschöpfung. Unbemannte fliegende Systeme können – für bestimmte Anwendungsgebiete – wesentlich günstiger, kleiner und effizienter ausgelegt werden als bemannte Lösungen. Dabei sind sich viele Unternehmen über das mögliche Potential dieser Technologie noch gar nicht bewusst.
The utilization of phase change material (PCM) for latent heat storage and thermal control of spacecraft has been demonstrated in the past in few missions only. One limiting factor was the fact that all concepts developed so far envisioned the PCM to be applied as an additional capacitor, encapsulated in its own housing, leading to mass, efficiency and accommodation challenges. Recently, the application of PCM within the scan cavity of a GEOS type satellite has been suggested, in order to tackle thermal issues due to direct sun intrusion (Choi, M., 2014). However, the application of PCM in such complex mechanical structures is extremely challenging. A new concept to tackle this issue is currently under development at the FH Aachen University of Applied Sciences. The concept "Infused Thermal Solutions (ITS)" is based on the idea to 3D print metallic structures in their regular functional shape, but double walled with internal lattice support structures, allowing the infusion of a PCM layer directly into the voids and eliminating the need for additional parts and interfaces. Together with OHB System, FH Aachen theoretically studied the application of this technology to the Meteosat Third Generation (MTG) Infra-Red Sounder (IRS) instrument. The study focuses on the scan cavity and entrance baffling assembly (EBA) of the IRS. It consists of thermal analyses, 3D-redesign and bread boarding of a scaled and PCM infused EBA version. In the thermal design of the alternative EBA, PCM was applied directly into the EBA, simulating the worst hot case sun intrusion of the mission. By applying 4kg of PCM (to a 60kg baffle) the EBA temperature excursions during sun intrusion were limited from 140K to 30K, leading to a significant thermo-opto-elastic performance gain. This paper introduces the ITS concept development status.
Bitcoin is a cryptocurrency and is considered a high-risk asset
class whose price changes are difficult to predict. Current research focusses
on daily price movements with a limited number of predictors. The paper at
hand aims at identifying measurable indicators for Bitcoin price movement s
and the development of a suitable forecasting model for hourly changes. The
paper provides three research contributions. First, a set of significant
indicators for predicting the Bitcoin price is identified. Second, the results of
a trained Long Short-term Memory (LSTM) neural network that predicts
price changes on an hourly basis is presented and compared with other
algorithms. Third, the results foster discussions of the applicability of neural
nets for stock price predictions. In total, 47 input features for a period of
over 10 months could be retrieved to train a neural net that predicts the
Bitcoin price movements with an error rate of 3.52 %.
Combined with the use of renewable energy sources for
its production, Hydrogen represents a possible alternative gas
turbine fuel for future low emission power generation. Due to
its different physical properties compared to other fuels such
as natural gas, well established gas turbine combustion
systems cannot be directly applied for Dry Low NOx (DLN)
Hydrogen combustion. This makes the development of new
combustion technologies an essential and challenging task
for the future of hydrogen fueled gas turbines.
The newly developed and successfully tested “DLN
Micromix” combustion technology offers a great potential to
burn hydrogen in gas turbines at very low NOx emissions.
Aiming to further develop an existing burner design in terms
of increased energy density, a redesign is required in order to
stabilise the flames at higher mass flows and to maintain low
emission levels.
For this purpose, a systematic design exploration has
been carried out with the support of CFD and optimisation
tools to identify the interactions of geometrical and design
parameters on the combustor performance. Aerodynamic
effects as well as flame and emission formation are observed
and understood time- and cost-efficiently. Correlations
between single geometric values, the pressure drop of the
burner and NOx production have been identified as a result.
This numeric methodology helps to reduce the effort of
manufacturing and testing to few designs for single
validation campaigns, in order to confirm the flame stability
and NOx emissions in a wider operating condition field.
Im Projekt wurden mit Hilfe zeitaufgeloester optischer Messtechniken Relaxations-Dynamiken von optisch angeregten Ladungstraegern und Hochfrequenzeigenschaften von Hochtemperatursupraleitern untersucht. Oberhalb der Sprungtemperatur wurden die Elektron-Phonon-Kopplungskonstanten fuer YBCO und BSCCO bestimmt. Dabei wurde erstmalig ein direkter Zusammenhang zwischen der Sprungtemperatur und der Kopplungstaerke gefunden. Der Kopplungsmechanismus enthaelt sowohl phononische als auch elektronische (spindynamische) Anteile. Unterhalb von T_c wird die Dynamik durch Aufbrechen und Rekombination von Cooper-Paaren bestimmt. Bei den Arbeiten zu den kohaerenten Phononen wurde ein Modell entwickelt, das das 'Anwerfen' der Phononen und das Amplitudenverhalten unterhalb der Sprungtemperatur erklaert. Als begleitende Untersuchungen wurden breitbandige Hochfrequenz-Messungen vorgenommen. Die Methode erlaubt die Untersuchung von Ladungstraegerdichten und von Streumechanismen. Erstmalig wurde in verspannten YBCO-Duennfilmen eine starke temperaturabhaengige Resonanz-Absorption beobachtet, die als Josephson-Plasmaresonanz an intrinsischen Kontakten identifiziert werden konnte. Die Nutzbarkeit dieser Kontakte fuer Bauelemente wurde durch einen Mikrowellendetektor demonstriert. Durch den Nachweis von emittierter gepulster Strahlung aus einer stromdurchflossenen supraleitenden Bruecke nach optischer Anregung wurde erstmals die Einsatzmoeglichkeit von HTSL fuer schnelle optische Schalter demonstriert. Es wurde gezeigt, dass die Schaltgeschwindigkeit eine direkte Folge der Ladungstraegerdynamik ist.
We present an automated pipeline for the generation of synthetic datasets for six-dimension (6D) object pose estimation. Therefore, a completely automated generation process based on predefined settings is developed, which enables the user to create large datasets with a minimum of interaction and which is feasible for applications with a high object variance. The pipeline is based on the Unreal 4 (UE4) game engine and provides a high variation for domain randomization, such as object appearance, ambient lighting, camera-object transformation and distractor density. In addition to the object pose and bounding box, the metadata includes all randomization parameters, which enables further studies on randomization parameter tuning. The developed workflow is adaptable to other 3D objects and UE4 environments. An exemplary dataset is provided including five objects of the Yale-CMU-Berkeley (YCB) object set. The datasets consist of 6 million subsegments using 97 rendering locations in 12 different UE4 environments. Each dataset subsegment includes one RGB image, one depth image and one class segmentation image at pixel-level.
Exercise training effectively mitigates aging-induced health and fitness impairments. Traditional training recommendations for the elderly focus separately on relevant physiological fitness domains, such as balance, flexibility, strength and endurance. Thus, a more holistic and functional training framework is needed. The proposed agility training concept integratively tackles spatial orientation, stop and go, balance and strength. The presented protocol aims at introducing a two-armed, one-year randomized controlled trial, evaluating the effects of this concept on neuromuscular, cardiovascular, cognitive and psychosocial health outcomes in healthy older adults. Eighty-five participants were enrolled in this ongoing trial. Seventy-nine participants completed baseline testing and were block-randomized to the agility training group or the inactive control group. All participants undergo pre- and post-testing with interim assessment after six months. The intervention group currently receives supervised, group-based agility training twice a week over one year, with progressively demanding perceptual, cognitive and physical exercises. Knee extension strength, reactive balance, dual task gait speed and the Agility Challenge for the Elderly (ACE) serve as primary endpoints and neuromuscular, cognitive, cardiovascular, and psychosocial meassures serve as surrogate secondary outcomes. Our protocol promotes a comprehensive exercise training concept for older adults, that might facilitate stakeholders in health and exercise to stimulate relevant health outcomes without relying on excessively time-consuming physical activity recommendations.
Bacterial cellulose (BC) is a promising material for biomedical applications due to its unique properties such as high mechanical strength and biocompatibility. This article describes the microbiological synthesis, modification, and characterization of the obtained BC-nanocomposites originating from symbiotic consortium Medusomyces gisevii. Two BC-modifications have been obtained: BC-Ag and BC-calcium phosphate (BC-Ca3(PO4)2). Structure and physicochemical properties of the BC and its modifications were investigated by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), atomic force microscopy (AFM), and infrared Fourier spectroscopy as well as by measurements of mechanical and water holding/absorbing capacities. Topographic analysis of the surface revealed multicomponent thick fibrils (150–160 nm in diameter and about 15 µm in length) constituted by 50–60 nm nanofibrils weaved into a left-hand helix. Distinctive features of Ca-phosphate-modified BC samples were (a) the presence of 500–700 nm entanglements and (b) inclusions of Ca3(PO4)2 crystals. The samples impregnated with Ag nanoparticles exhibited numerous roundish inclusions, about 110 nm in diameter. The boundaries between the organic and inorganic phases were very distinct in both cases. The Ag-modified samples also showed a prominent waving pattern in the packing of nanofibrils. The obtained BC gel films possessed water-holding capacity of about 62.35 g/g. However, the dried (to a constant mass) BC-films later exhibited a low water absorption capacity (3.82 g/g). It was found that decellularized BC samples had 2.4 times larger Young’s modulus and 2.2 times greater tensile strength as compared to dehydrated native BC films. We presume that this was caused by molecular compaction of the BC structure.