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A novel method to determine the extruded length of a metallic wire for a directed energy deposition (DED) process using a microwave (MW) plasma jet with a straight-through wire feed is presented. The method is based on the relative comparison of the measured frequency response obtained by the large-signal scattering parameter (Hot-S) technique. In the practical working range, repeatability of less than 6% for a nonactive plasma and 9% for the active plasma state is found. Measurements are conducted with a focus on a simple solution to decrease the processing time and reduce the integration time of the process into the existing hardware. It is shown that monitoring a single frequency for magnitude and phase changes is sufficient to achieve good accuracy. A combination of different measurement values to determine the length is possible. The applicability to different diameter of the same material is shown as well as a contact detection of the wire and metallic substrate.
This paper describes the development of a capacitively coupled high-pressure lamp with input power between 20 and 43 W at 2.45 GHz, using a coaxial line network. Compared with other electrodeless lamp systems, no cavity has to be used and a reduction in the input power is achieved. Therefore, this lamp is an alternative to the halogen incandescent lamp for domestic lighting. To serve the demands of domestic lighting, the filling of the lamp is optimized over all other resulting requirements, such as high efficacy at low induced powers and fast startups. A workflow to develop RF-driven plasma applications is presented, which makes use of the hot S-parameter technique. Descriptions of the fitting process inside a circuit and FEM simulator are given. Results of the combined ignition and operation network from simulations and measurements are compared. An initial prototype is built and measurements of the lamp's lighting properties are presented along with an investigation of the efficacy optimizations using large signal amplitude modulation. With this lamp, an efficacy of 135 lmW -1 is achieved.
Thermal and Optical Study on the Frequency Dependence of an Atmospheric Microwave Argon Plasma Jet
(2019)
Critical quantitative evaluation of integrated health management methods for fuel cell applications
(2024)
Online fault diagnostics is a crucial consideration for fuel cell systems, particularly in mobile applications, to limit downtime and degradation, and to increase lifetime. Guided by a critical literature review, in this paper an overview of Health management systems classified in a scheme is presented, introducing commonly utilised methods to diagnose FCs in various applications. In this novel scheme, various Health management system methods are summarised and structured to provide an overview of existing systems including their associated tools. These systems are classified into four categories mainly focused on model-based and non-model-based systems. The individual methods are critically discussed when used individually or combined aimed at further understanding their functionality and suitability in different applications. Additionally, a tool is introduced to evaluate methods from each category based on the scheme presented. This tool applies the technique of matrix evaluation utilising several key parameters to identify the most appropriate methods for a given application. Based on this evaluation, the most suitable methods for each specific application are combined to build an integrated Health management system.
Quantitative evaluation of health management designs for fuel cell systems in transport vehicles
(2022)
Focusing on transport vehicles, mainly with regard to aviation applications, this paper presents compilation and subsequent quantitative evaluation of methods aimed at building an optimum integrated health management solution for fuel cell systems. The methods are divided into two different main types and compiled in a related scheme. Furthermore, different methods are analysed and evaluated based on parameters specific to the aviation context of this study. Finally, the most suitable method for use in fuel cell health management systems is identified and its performance and suitability is quantified.
In this work, the effect of low air relative humidity on the operation of a polymer electrolyte membrane fuel cell is investigated. An innovative method through performing in situ electrochemical impedance spectroscopy is utilised to quantify the effect of inlet air relative humidity at the cathode side on internal ionic resistances and output voltage of the fuel cell. In addition, algorithms are developed to analyse the electrochemical characteristics of the fuel cell. For the specific fuel cell stack used in this study, the membrane resistance drops by over 39 % and the cathode side charge transfer resistance decreases by 23 % after increasing the humidity from 30 % to 85 %, while the results of static operation also show an increase of ∼2.2 % in the voltage output after increasing the relative humidity from 30 % to 85 %. In dynamic operation, visible drying effects occur at < 50 % relative humidity, whereby the increase of the air side stoichiometry increases the drying effects. Furthermore, other parameters, such as hydrogen humidification, internal stack structure, and operating parameters like stoichiometry, pressure, and temperature affect the overall water balance. Therefore, the optimal humidification range must be determined by considering all these parameters to maximise the fuel cell performance and durability. The results of this study are used to develop a health management system to ensure sufficient humidification by continuously monitoring the fuel cell polarisation data and electrochemical impedance spectroscopy indicators.
In addition to electromobility and alternative drive systems, a focus is set on electrically driven compressors (EDC), with a high potential for increasing the efficiency of internal combustion engines (ICE) and fuel cells [01]. The primary objective is to increase the ICE torque, provided independently of the ICE speed by compressing the intake air and consequently the ICE filling level supported by the compressor. For operation independent from the ICE speed, the EDC compressor is decoupled from the turbine by using an electric compressor motor (CM) instead of the turbine. ICE performances can be increased by the use of EDC where individual compressor parameters are adapted to the respective application area [02] [03]. This task contains great challenges, increased by demands with regard to pollutant reduction while maintaining constant performance and reduced fuel consumption. The FH-Aachen is equipped with an EDC test bench which enables EDC-investigations in various configurations and operating modes. Characteristic properties of different compressors can be determined, which build the basis for a comparison methodology. Subject of this project is the development of a comparison methodology for EDC with an associated evaluation method and a defined overall evaluation method. For the application of this comparison methodology, corresponding series of measurements are carried out on the EDC test bench using an appropriate test device.
Quantitative Farbmessung in laryngoskopischen Bildern. Palm, C; Scholl, I; Lehmann, TM; Spitzer, K.
(1998)
MedicVR : Acceleration and Enhancement Techniques for Direct Volume Rendering in Virtual Reality
(2019)
In this paper, carbon nanotubes (CNTs) were incorporated in penicillinase-phospholipid Langmuir and Langmuir–Blodgett (LB) films to enhance the enzyme catalytic properties. Adsorption of the penicillinase and CNTs at dimyristoylphosphatidic acid (DMPA) monolayers at the air–water interface was investigated by surface pressure–area isotherms, vibrational spectroscopy, and Brewster angle microscopy. The floating monolayers were transferred to solid supports through the LB technique, forming mixed DMPA-CNTs-PEN films, which were investigated by quartz crystal microbalance, vibrational spectroscopy, and atomic force microscopy. Enzyme activity was studied with UV–vis spectroscopy and the feasibility of the supramolecular device nanostructured as ultrathin films were essayed in a capacitive electrolyte–insulator–semiconductor (EIS) sensor device. The presence of CNTs in the enzyme–lipid LB film not only tuned the catalytic activity of penicillinase but also helped conserve its enzyme activity after weeks, showing increased values of activity. Viability as penicillin sensor was demonstrated with capacitance/voltage and constant capacitance measurements, exhibiting regular and distinctive output signals over all concentrations used in this work. These results may be related not only to the nanostructured system provided by the film, but also to the synergism between the compounds on the active layer, leading to a surface morphology that allowed a fast analyte diffusion because of an adequate molecular accommodation, which also preserved the penicillinase activity. This work therefore demonstrates the feasibility of employing LB films composed of lipids, CNTs, and enzymes as EIS devices for biosensing applications.
Muscle function is compromised by gravitational unloading in space affecting overall musculoskeletal health. Astronauts perform daily exercise programmes to mitigate these effects but knowing which muscles to target would optimise effectiveness. Accurate inflight assessment to inform exercise programmes is critical due to lack of technologies suitable for spaceflight. Changes in mechanical properties indicate muscle health status and can be measured rapidly and non-invasively using novel technology. A hand-held MyotonPRO device enabled monitoring of muscle health for the first time in spaceflight (> 180 days). Greater/maintained stiffness indicated countermeasures were effective. Tissue stiffness was preserved in the majority of muscles (neck, shoulder, back, thigh) but Tibialis Anterior (foot lever muscle) stiffness decreased inflight vs. preflight (p < 0.0001; mean difference 149 N/m) in all 12 crewmembers. The calf muscles showed opposing effects, Gastrocnemius increasing in stiffness Soleus decreasing. Selective stiffness decrements indicate lack of preservation despite daily inflight countermeasures. This calls for more targeted exercises for lower leg muscles with vital roles as ankle joint stabilizers and in gait. Muscle stiffness is a digital biomarker for risk monitoring during future planetary explorations (Moon, Mars), for healthcare management in challenging environments or clinical disorders in people on Earth, to enable effective tailored exercise programmes.
Recently, novel AI-based services have emerged in the consumer market. AI-based services can affect the way consumers take commercial decisions. Research on the influence of AI on commercial interactions is in its infancy. In this chapter, a framework creating a first overview of the influence of AI on commercial interactions is introduced. This framework summarizes the findings of comparing numerous customer journeys of novel AI-based services with corresponding non-AI equivalents.
In the context of the increasing digitalization, the Internet of Things (IoT) is seen as a technological driver through which completely new business models can emerge in the interaction of different players. Identified key players include traditional industrial companies, municipalities and telecommunications companies. The latter, by providing connectivity, ensure that small devices with tiny batteries can be connected almost anywhere and directly to the Internet. There are already many IoT use cases on the market that provide simplification for end users, such as Philips Hue Tap. In addition to business models based on connectivity, there is great potential for information-driven business models that can support or enhance existing business models. One example is the IoT use case Park and Joy, which uses sensors to connect parking spaces and inform drivers about available parking spaces in real time. Information-driven business models can be based on data generated in IoT use cases. For example, a telecommunications company can add value by deriving more decision-relevant information – called insights – from data that is used to increase decision agility. In addition, insights can be monetized. The monetization of insights can only be sustainable, if careful attention is taken and frameworks are considered. In this chapter, the concept of information-driven business models is explained and illustrated with the concrete use case Park and Joy. In addition, the benefits, risks and framework conditions are discussed.
This paper presents a thermal simulation environment for moving objects on the lunar surface. The goal of the thermal simulation environment is to enable the reliable prediction of the temperature development of a given object on the lunar surface by providing the respective heat fluxes for a mission on a given travel path. The user can import any object geometry and freely define the path that the object should travel. Using the path of the object, the relevant lunar surface geometry is imported from a digital elevation model. The relevant parts of the lunar surface are determined based on distance to the defined path. A thermal model of these surface sections is generated, consisting of a porous layer on top and a denser layer below. The object is moved across the lunar surface, and its inclination is adapted depending on the slope of the terrain below it. Finally, a transient thermal analysis of the object and its environment is performed at several positions on its path and the results are visualized. The paper introduces details on the thermal modeling of the lunar surface, as well as its verification. Furthermore, the structure of the created software is presented. The robustness of the environment is verified with the help of sensitivity studies and possible improvements are presented.
We analyze the trading behavior of individual investors in option-like securities, namely bankissued warrants, and thus expand the growing literature of investors behavior to a new kind of securities. A unique data set from a large German discount broker gives us the opportunity to analyze the trading behavior of 1,454 investors, making 89,958 transactions in 6,724 warrants on 397 underlyings. In different logit regression, we make use of the facts that investors can speculate on rising and falling prices of the underlying with call and put warrants and that we also have information about the stock portfolios of the investors. We report several facts about the trading behavior of individual investors in warrants that are consistent with the literature on the behavior of individual investors in the stock market. The warrant investors buy calls and sell puts if the price of the underlying has decreased over the past trading days and they sell calls and buy puts if the price of the underlying has increased. That means, the investors follow negative feedback trading strategies in all four trading categories observed. In addition, we find strong evidence for the disposition effect for call as well as put warrants, which is reversed in December. The trading behavior is also influenced if the underlying reaches some exceptionally prices, e.g. highs, lows or the strike price. We show that hedging, as one natural candidate to buy puts, does not play an important role in the market for bank-issued warrants.
Due to the high number of customer contacts, fault clearances, installations, and product provisioning per year, the automation level of operational processes has a significant impact on financial results, quality, and customer experience. Therefore, the telecommunications operator Deutsche Telekom (DT) has defined a digital strategy with the objectives of zero complexity and zero complaint, one touch, agility in service, and disruptive thinking. In this context, Robotic Process Automation (RPA) was identified as an enabling technology to formulate and realize DT’s digital strategy through automation of rule-based, routine, and predictable tasks in combination with structured and stable data.
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.
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.
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.