Refine
Year of publication
- 2020 (171) (remove)
Institute
- Fachbereich Medizintechnik und Technomathematik (57)
- IfB - Institut für Bioengineering (33)
- Fachbereich Luft- und Raumfahrttechnik (30)
- Fachbereich Energietechnik (25)
- Fachbereich Elektrotechnik und Informationstechnik (20)
- ECSM European Center for Sustainable Mobility (14)
- Fachbereich Maschinenbau und Mechatronik (11)
- Fachbereich Wirtschaftswissenschaften (11)
- INB - Institut für Nano- und Biotechnologien (11)
- Fachbereich Chemie und Biotechnologie (10)
Language
- English (171) (remove)
Document Type
- Article (101)
- Conference Proceeding (46)
- Part of a Book (16)
- Book (2)
- Conference Poster (2)
- Doctoral Thesis (2)
- Conference: Meeting Abstract (1)
- Other (1)
Keywords
- MINLP (3)
- Additive manufacturing (2)
- Adjacent buildings (2)
- Experimental validation (2)
- Historical centres (2)
- Shake table test (2)
- Stone masonry (2)
- rebound-effect (2)
- sustainability (2)
- 3D printing (1)
Is part of the Bibliography
- no (171)
Reinforced concrete (RC) structures with masonry infills are widely used for several types of buildings all over the world. However, it is well known that traditional masonry infills constructed with rigid contact to the surrounding RC frame performed rather poor in past earthquakes. Masonry infills showed severe in-plane damages and failed in many cases under out-of-plane seismic loading. As the undesired interactions between frames and infills changes the load transfer on building level, complete collapses of buildings were observed. A possible solution is uncoupling of masonry infills to the frame to reduce the infill contribution activated by the frame deformation under horizontal loading. The paper presents numerical simulations on RC frames equipped with the innovative decoupling system INODIS. The system was developed within the European project INSYSME and allows an effective uncoupling of frame and infill. The simulations are carried out with a micro-modelling approach, which is able to predict the complex nonlinear behaviour resulting from the different materials and their interaction. Each brick is modelled individually and connected taking into account nonlinearity of a brick mortar interface. The calibration of the model is based on small specimen tests and experimental results for one bay one storey frame are used for the validation. The validated model is further used for parametric studies on two storey and two bay infilled frames. The response and change of the structural stiffness are analysed and compared to the traditionally infilled frame. The results confirm the effectiveness of the INODIS system with less damage and relatively low contribution of the infill at high drift levels. In contrast to the uncoupled system configurations, traditionally infilled frames experienced brittle failure at rather low drift levels.
Coronavirus disease 2019 (COVID-19) is a novel human infectious disease provoked by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, no specific vaccines or drugs against COVID-19 are available. Therefore, early diagnosis and treatment are essential in order to slow the virus spread and to contain the disease outbreak. Hence, new diagnostic tests and devices for virus detection in clinical samples that are faster, more accurate and reliable, easier and cost-efficient than existing ones are needed. Due to the small sizes, fast response time, label-free operation without the need for expensive and time-consuming labeling steps, the possibility of real-time and multiplexed measurements, robustness and portability (point-of-care and on-site testing), biosensors based on semiconductor field-effect devices (FEDs) are one of the most attractive platforms for an electrical detection of charged biomolecules and bioparticles by their intrinsic charge. In this review, recent advances and key developments in the field of label-free detection of viruses (including plant viruses) with various types of FEDs are presented. In recent years, however, certain plant viruses have also attracted additional interest for biosensor layouts: Their repetitive protein subunits arranged at nanometric spacing can be employed for coupling functional molecules. If used as adapters on sensor chip surfaces, they allow an efficient immobilization of analyte-specific recognition and detector elements such as antibodies and enzymes at highest surface densities. The display on plant viral bionanoparticles may also lead to long-time stabilization of sensor molecules upon repeated uses and has the potential to increase sensor performance substantially, compared to conventional layouts. This has been demonstrated in different proof-of-concept biosensor devices. Therefore, richly available plant viral particles, non-pathogenic for animals or humans, might gain novel importance if applied in receptor layers of FEDs. These perspectives are explained and discussed with regard to future detection strategies for COVID-19 and related viral diseases.
The paper presents an aerodynamic investigation of 70 different streamlined bodies with fineness ratios ranging from 2 to 10. The bodies are chosen to idealize both unmanned and small manned aircraft fuselages and feature cross-sectional shapes that vary from circular to quadratic. The study focuses on friction and pressure drag in dependency of the individual body’s fineness ratio and cross section. The drag forces are normalized with the respective body’s wetted area to comply with an empirical drag estimation procedure. Although the friction drag coefficient then stays rather constant for all bodies, their pressure drag coefficients decrease with an increase in fineness ratio. Referring the pressure drag coefficient to the bodies’ cross-sectional areas shows a distinct pressure drag minimum at a fineness ratio of about three. The pressure drag of bodies with a quadratic cross section is generally higher than for bodies of revolution. The results are used to derive an improved form factor that can be employed in a classic empirical drag estimation method. The improved formulation takes both the fineness ratio and cross-sectional shape into account. It shows superior accuracy in estimating streamlined body drag when compared with experimental data and other form factor formulations of the literature.
Additive manufacturing (AM) works by creating objects layer by layer in a manner similar to a 2D printer with the “printed” layers stacked on top of each other. The layer-wise manufacturing nature of AM enables fabrication of freeform geometries which cannot be fabricated using conventional manufacturing methods as a one part. Depending on how each layer is created and bonded to the adjacent layers, different AM methods have been developed. In this chapter, the basic terms, common materials, and different methods of AM are described, and their potential applications are discussed.
The implementation of IO-Link in the automation industry has increased over the years. Its main advantage is it offers a digital point-to-point plugand-play interface for any type of device or application. This simplifies the communication between devices and increases productivity with its different features like self-parametrization and maintenance. However, its complete potential is not always used.
The aim of this paper is to create an Arduino based framework for the development of generic IO-Link devices and increase its implementation for rapid prototyping. By generating the IO device description file (IODD) from a graphical user interface, and further customizable options for the device application, the end-user can intuitively develop generic IO-Link devices. The peculiarity of this framework relies on its simplicity and abstraction which allows to implement any sensor functionality and virtually connect any type of device to an IO-Link master. This work consists of the general overview of the framework, the technical background of its development and a proof of concept which demonstrates the workflow for its implementation.
The production of dispatchable renewable energy will be one of the most important key factors of the future energy supply. Concentrated solar power (CSP) plants operated with molten salt as heat transfer and storage media are one opportunity to meet this challenge. Due to the high concentration factor of the solar tower technology the maximum process temperature can be further increased which ultimately decreases the levelized costs of electricity of the technology (LCOE). The development of an improved tubular molten salt receiver for the next generation of molten salt solar tower plants is the aim of this work. The receiver is designed for a receiver outlet temperature up to 600 °C. Together with a complete molten salt system, the receiver will be integrated into the Multi-Focus-Tower (MFT) in Jülich (Germany). The paper describes the basic engineering of the receiver, the molten salt tower system and a laboratory corrosion setup.
The paper presents a method for the quantitative assessment of choroidal blood flow using an OCT-A system. The developed technique for processing of OCT-A scans is divided into two stages. At the first stage, the identification of the boundaries in the selected portion was performed. At the second stage, each pixel mark on the selected layer was represented as a volume unit, a voxel, which characterizes the region of moving blood. Three geometric shapes were considered to represent the voxel. On the example of one OCT-A scan, this work presents a quantitative assessment of the blood flow index. A possible modification of two-stage algorithm based on voxel scan processing is presented.
In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem.
Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ.
Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible.
In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production.
Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data.
The adoption of the Digital Health Transformation is a tremendous paradigm change in health organizations, which is not a trivial process in reality. For that reason, in this chapter, it is proposed a methodology with the objective to generate a changing culture in healthcare organisations. Such a change culture is essential for the successful implementation of any supporting methods like Interactive Process Mining. It needs to incorporate (mostly) new ways of team-based and evidence-based approaches for solving structural problems in a digital healthcare environment.
Muscular activity in terms of surface electromyography (sEMG) is usually normalised to maximal voluntary isometric contractions (MVICs). This study aims to compare two different MVIC-modes in handcycling and examine the effect of moving average window-size. Twelve able-bodied male competitive triathletes performed ten MVICs against manual resistance and four sport-specific trials against fixed cranks. sEMG of ten muscles [M. trapezius (TD); M. pectoralis major (PM); M. deltoideus, Pars clavicularis (DA); M. deltoideus, Pars spinalis (DP); M. biceps brachii (BB); M. triceps brachii (TB); forearm flexors (FC); forearm extensors (EC); M. latissimus dorsi (LD) and M. rectus abdominis (RA)] was recorded and filtered using moving average window-sizes of 150, 200, 250 and 300 ms. Sport-specific MVICs were higher compared to manual resistance for TB, DA, DP and LD, whereas FC, TD, BB and RA demonstrated lower values. PM and EC demonstrated no significant difference between MVIC-modes. Moving average window-size had no effect on MVIC outcomes. MVIC-mode should be taken into account when normalised sEMG data are illustrated in handcycling. Sport-specific MVICs seem to be suitable for some muscles (TB, DA, DP and LD), but should be augmented by MVICs against manual/mechanical resistance for FC, TD, BB and RA.
Stored and cooled, highly-charged ions offer unprecedented capabilities for precision studies in the realm of atomic, nuclear structure and astrophysics[1]. After the successful investigation of the 96Ru(p,7)97Rh reaction cross section in 2009[2], the first measurement of the 124Xe(p,7)125Cs reaction cross section has been performed with decelerated, fully-ionized 124Xe ions in 2016 at the Experimental Storage Ring (ESR) of GSI[3]. Using a Double Sided Silicon Strip Detector, introduced directly into the ultra-high vacuum environment of a storage ring, the 125Cs proton-capture products have been successfully detected. The cross section has been measured at 5 different energies between 5.5AMeV and 8AMeV, on the high energy tail of the Gamow-window for hot, explosive scenarios such as supernovae and X-ray binaries. The elastic scattering on the H2 gas jet target is the major source of background to count the (p,7) events. Monte Carlo simulations show that an additional slit system in the ESR in combination with the energy information of the Si detector will enable background free measurements of the proton-capture products. The corresponding hardware is being prepared and will increase the sensitivity of the method tremendously.
Cross sections for neutron-induced reactions of short-lived nuclei are essential for nuclear astrophysics since these reactions in the stars are responsible for the production of most heavy elements in the universe. These reactions are also key in applied domains like energy production and medicine. Nevertheless, neutron-induced cross-section measurements can be extremely challenging or even impossible to perform due to the radioactivity of the targets involved. Indirect measurements through the surrogate-reaction method can help to overcome these difficulties.
The surrogate-reaction method relies on the use of an alternative reaction that will lead to the formation of the same excited nucleus as in the neutron-induced reaction of interest. The decay probabilities (for fission, neutron and gamma-ray emission) of the nucleus produced via the surrogate reaction allow one to constrain models and the prediction of the desired neutron cross sections.
We propose to perform surrogate reaction measurements in inverse kinematics at heavy-ion storage rings, in particular at the CRYRING@ESR of the GSI/FAIR facility. We present the conceptual idea of the most promising setup to measure for the first time simultaneously the fission, neutron and gamma-ray emission probabilities. The results of the first simulations considering the 238U(d,d') reaction are shown, as well as new technical developments that are being carried out towards this set-up.
This paper analyzes the drag characteristics of several landing gear and turret configurations that are representative of unmanned aircraft tricycle landing gears and sensor turrets. A variety of these components were constructed via 3D-printing and analyzed in a wind-tunnel measurement campaign. Both turrets and landing gears were attached to a modular fuselage that supported both isolated components and multiple components at a time. Selected cases were numerically investigated with a Reynolds-averaged Navier-Stokes approach that showed good accuracy when compared to wind-tunnel data. The drag of main gear struts could be significantly reduced via streamlining their cross-sectional shape and keeping load carrying capabilities similar. The attachment of wheels introduced interference effects that increased strut drag moderately but significantly increased wheel drag compared to isolated cases. Very similar behavior was identified for front landing gears. The drag of an electro-optical and infrared sensor turret was found to be much higher than compared to available data of a clean hemisphere-cylinder combination. This turret drag was merely influenced by geometrical features like sensor surfaces and the rotational mechanism. The new data of this study is used to develop simple drag estimation recommendations for main and front landing gear struts and wheels as well as sensor turrets. These recommendations take geometrical considerations and interference effects into account.
The predictive control of commercial vehicle energy management systems, such as vehicle thermal management or waste heat recovery (WHR) systems, are discussed on the basis of information sources from the field of environment recognition and in combination with the determination of the vehicle system condition.
In this article, a mathematical method for predicting the exhaust gas mass flow and the exhaust gas temperature is presented based on driving data of a heavy-duty vehicle. The prediction refers to the conditions of the exhaust gas at the inlet of the exhaust gas recirculation (EGR) cooler and at the outlet of the exhaust gas aftertreatment system (EAT). The heavy-duty vehicle was operated on the motorway to investigate the characteristic operational profile. In addition to the use of road gradient profile data, an evaluation of the continuously recorded distance signal, which represents the distance between the test vehicle and the road user ahead, is included in the prediction model. Using a Fourier analysis, the trajectory of the vehicle speed is determined for a defined prediction horizon.
To verify the method, a holistic simulation model consisting of several hierarchically structured submodels has been developed. A map-based submodel of a combustion engine is used to determine the EGR and EAT exhaust gas mass flows and exhaust gas temperature profiles. All simulation results are validated on the basis of the recorded vehicle and environmental data. Deviations from the predicted values are analyzed and discussed.
The paper presents the derivation of a new equivalent skin friction coefficient for estimating the parasitic drag of short-to-medium range fixed-wing unmanned aircraft. The new coefficient is derived from an aerodynamic analysis of ten different unmanned aircraft used on surveillance, reconnaissance, and search and rescue missions. The aircraft are simulated using a validated unsteady Reynolds-averaged Navier Stokes approach. The UAV's parasitic drag is significantly influenced by the presence of miscellaneous components like fixed landing gears or electro-optical sensor turrets. These components are responsible for almost half of an unmanned aircraft's total parasitic drag. The new equivalent skin friction coefficient accounts for these effects and is significantly higher compared to other aircraft categories. It is used to initially size an unmanned aircraft for a typical reconnaissance mission. The improved parasitic drag estimation yields a much heavier unmanned aircraft when compared to the sizing results using available drag data of manned aircraft.