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Experimental and numerical investigation on the effect of pressure on micromix hydrogen combustion
(2021)
The micromix (MMX) combustion concept is a DLN gas turbine combustion technology designed for high hydrogen content fuels. Multiple non-premixed miniaturized flames based on jet in cross-flow (JICF) are inherently safe against flashback and ensure a stable operation in various operative conditions.
The objective of this paper is to investigate the influence of pressure on the micromix flame with focus on the flame initiation point and the NOx emissions. A numerical model based on a steady RANS approach and the Complex Chemistry model with relevant reactions of the GRI 3.0 mechanism is used to predict the reactive flow and NOx emissions at various pressure conditions. Regarding the turbulence-chemical interaction, the Laminar Flame Concept (LFC) and the Eddy Dissipation Concept (EDC) are compared. The numerical results are validated against experimental results that have been acquired at a high pressure test facility for industrial can-type gas turbine combustors with regard to flame initiation and NOx emissions.
The numerical approach is adequate to predict the flame initiation point and NOx emission trends. Interestingly, the flame shifts its initiation point during the pressure increase in upstream direction, whereby the flame attachment shifts from anchoring behind a downstream located bluff body towards anchoring directly at the hydrogen jet. The LFC predicts this change and the NOx emissions more accurately than the EDC. The resulting NOx correlation regarding the pressure is similar to a non-premixed type combustion configuration.
Experimental investigation of behaviour of masonry infilled RC frames under out-of-plane loading
(2021)
Masonry infills are commonly used as exterior or interior walls in reinforced concrete (RC) frame structures and they can be encountered all over the world, including earthquake prone regions. Since the middle of the 20th century the behaviour of these non-structural elements under seismic loading has been studied in numerous experimental campaigns. However, most of the studies were carried out by means of in-plane tests, while there is a lack of out-of-plane experimental investigations. In this paper, the out-of-plane tests carried out on full scale masonry infilled frames are described. The results of the out-of-plane tests are presented in terms of force-displacement curves and measured out-of-plane displacements. Finally, the reliability of existing analytical approaches developed to estimate the out-of-plane strength of masonry infills is examined on presented experimental results.
Experimental investigation of selective laser melting of lunar regolith for in-situ applications
(2013)
Past earthquakes demonstrated the high vulnerability of industrial facilities equipped with complex process technologies leading to serious damage of the process equipment and multiple and simultaneous release of hazardous substances in industrial facilities. Nevertheless, the design of industrial plants is inadequately described in recent codes and guidelines, as they do not consider the dynamic interaction between the structure and the installations and thus the effect of seismic response of the installations on the response of the structure and vice versa. The current code-based approach for the seismic design of industrial facilities is considered not enough for ensure proper safety conditions against exceptional event entailing loss of content and related consequences. Accordingly, SPIF project (Seismic Performance of Multi-Component Systems in Special Risk Industrial Facilities) was proposed within the framework of the European H2020 - SERA funding scheme (Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe). The objective of the SPIF project is the investigation of the seismic behaviour of a representative industrial structure equipped with complex process technology by means of shaking table tests. The test structure is a three-story moment resisting steel frame with vertical and horizontal vessels and cabinets, arranged on the three levels and connected by pipes. The dynamic behaviour of the test structure and of its relative several installations is investigated. Furthermore, both process components and primary structure interactions are considered and analyzed. Several PGA-scaled artificial ground motions are applied to study the seismic response at different levels. After each test, dynamic identification measurements are carried out to characterize the system condition. The contribution presents the experimental setup of the investigated structure and installations, selected measurement data and describes the obtained damage. Furthermore, important findings for the definition of performance limits, the effectiveness of floor response spectra in industrial facilities will be presented and discussed.
In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions.
We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.
Messenger apps like WhatsApp or Telegram are an integral part of daily communication. Besides the various positive effects, those services extend the operating range of criminals. Open trading groups with many thousand participants emerged on Telegram. Law enforcement agencies monitor suspicious users in such chat rooms. This research shows that text analysis, based on natural language processing, facilitates this through a meaningful domain overview and detailed investigations. We crawled a corpus from such self-proclaimed black markets and annotated five attribute types products, money, payment methods, user names, and locations. Based on each message a user sends, we extract and group these attributes to build profiles. Then, we build features to cluster the profiles. Pretrained word vectors yield better unsupervised clustering results than current
state-of-the-art transformer models. The result is a semantically meaningful high-level overview of the user landscape of black market chatrooms. Additionally, the extracted structured information serves as a foundation for further data exploration, for example, the most active users or preferred payment methods.
Extrem hohe Blitzströme
(2017)
Future engineers are increasingly confronted with the so-called Megatrends which are the big social challenges society has to cope with. These Megatrends, such as “Silver Society”, “Globalization”, “Mobility” and “Female Shift” require an application-oriented perspective on Diversity especially in the engineering field. Therefore, it is necessary to enable future engineers not only to look at the technical perspectives of a problem, but also to be able to see the related questions within societies they are developing their artefacts for. The aim of teaching engineering should be to prepare engineers for these requirements and to draw attention to the diverse needs in a globalized world.
Bringing together technical knowledge and social competences which go beyond a mere training of the so-called “soft skills”, is a new approach followed at RWTH Aachen University, one of the leading technical universities in Germany. RWTH Aachen University has established the bridging professorship “Gender and Diversity in Engineering” (GDI) which educates engineers with an interdisciplinary approach to expand engineering limits. In the frame of a sustainable teaching concept the research group under the leadership of Prof. Carmen Leicht-Scholten has developed an approach which imparts a supplication-specific Gender and Diversity expertise to engineers. In workshops students gain theoretical knowledge about Gender and Diversity and learn how to transfer their knowledge in their special field of study and later work. To substantiate this, the course participants have to solve case studies from real life. The cases which are developed in collaboration with non-profit organizations and enterprises from economy rise the students to challenges which are inspired by professional life. Evaluation shows the success of this approach as well as an increasing demand for such teaching formats.
A new formulation to calculate the shakedown limit load of Kirchhoff plates under stochastic conditions of strength is developed. Direct structural reliability design by chance con-strained programming is based on the prescribed failure probabilities, which is an effective approach of stochastic programming if it can be formulated as an equivalent deterministic optimization problem. We restrict uncertainty to strength, the loading is still deterministic. A new formulation is derived in case of random strength with lognormal distribution. Upper bound and lower bound shakedown load factors are calculated simultaneously by a dual algorithm.
The structure of the female pelvic floor (PF) is an inter-related system of bony pelvis,muscles, pelvic organs, fascias, ligaments, and nerves with multiple functions. Mechanically, thepelvic organ support system are of two types: (I) supporting system of the levator ani (LA) muscle,and (II) the suspension system of the endopelvic fascia condensation [1], [2]. Significantdenervation injury to the pelvic musculature, depolimerization of the collagen fibrils of the softvaginal hammock, cervical ring and ligaments during pregnancy and vaginal delivery weakens thenormal functions of the pelvic floor. Pelvic organ prolapse, incontinence, sexual dysfunction aresome of the dysfunctions which increases progressively with age and menopause due toweakened support system according to the Integral theory [3]. An improved 3D finite elementmodel of the female pelvic floor as shown in Fig. 1 is constructed that: (I) considers the realisticsupport of the organs to the pelvic side walls, (II) employs the improvement of our previous FEmodel [4], [5] along with the patient based geometries, (III) incorporates the realistic anatomy andboundary conditions of the endopelvic (pubocervical and rectovaginal) fascia, and (IV) considersvarying stiffness of the endopelvic fascia in the craniocaudal direction [3]. Several computationsare carried out on the presented computational model with healthy and damaged supportingtissues, and comparisons are made to understand the physiopathology of the female PF disorders.
Flexible Fuel Operation of a Dry-Low-Nox Micromix Combustor with Variable Hydrogen Methane Mixtures
(2019)
Flight times to the heliopause using a combination of solar and radioisotope electric propulsion
(2011)
We investigate the interplanetary flight of a low-thrust space probe to the heliopause,located at a distance of about 200 AU from the Sun. Our goal was to reach this distance within the 25 years postulated by ESA for such a mission (which is less ambitious than the 15-year goal set by NASA). Contrary to solar sail concepts and combinations of allistic and electrically propelled flight legs, we have investigated whether the set flight time limit could also be kept with a combination of solar-electric propulsion and a second, RTG-powered upper stage. The used ion engine type was the RIT-22 for the first stage and the RIT-10 for the second stage. Trajectory optimization was carried out with the low-thrust optimization program InTrance, which implements the method of Evolutionary Neurocontrol,using Artificial Neural Networks for spacecraft steering and Evolutionary Algorithms to optimize the Neural Networks’ parameter set. Based on a parameter space study, in which the number of thrust units, the unit’s specific impulse, and the relative size of the solar power generator were varied, we have chosen one configuration as reference. The transfer time of this reference configuration was 29.6 years and the fastest one, which is technically
more challenging, still required 28.3 years. As all flight times of this parameter study were longer than 25 years, we further shortened the transfer time by applying a launcher-provided hyperbolic excess energy up to 49 km2/s2. The resulting minimal flight time for the reference configuration was then 27.8 years. The following, more precise optimization to a launch with the European Ariane 5 ECA rocket reduced the transfer time to 27.5 years. This is the fastest mission design of our study that is flexible enough to allow a launch every
year. The inclusion of a fly-by at Jupiter finally resulted in a flight time of 23.8 years,which is below the set transfer-time limit. However, compared to the 27.5-year transfer,this mission design has a significantly reduced launch window and mission flexibility if the
escape direction is restricted to the heliosphere’s “nose".
Useful market simulations are key to the evaluation of diferent market designs existing of multiple market mechanisms or rules. Yet a simulation framework which has a comparison of diferent market mechanisms in mind was not found. The need to create an objective view on different sets of market rules while investigating meaningful agent strategies concludes that such a simulation framework is needed to advance the research on this subject. An overview of diferent existing market simulation models is given which also shows the research gap and the missing capabilities of those systems. Finally, a methodology is outlined how a novel market simulation which can answer the research questions can be developed.
Frequency Dependent Impedance Analysis of the Foundation-Soil-Systems of Onshore Wind Turbines
(2018)
Messenger apps like WhatsApp and Telegram are frequently used for everyday communication, but they can also be utilized as a platform for illegal activity. Telegram allows public groups with up to 200.000 participants. Criminals use these public groups for trading illegal commodities and services, which becomes a concern for law enforcement agencies, who manually monitor suspicious activity in these chat rooms. This research demonstrates how natural language processing (NLP) can assist in analyzing these chat rooms, providing an explorative overview of the domain and facilitating purposeful analyses of user behavior. We provide a publicly available corpus of annotated text messages with entities and relations from four self-proclaimed black market chat rooms. Our pipeline approach aggregates the extracted product attributes from user messages to profiles and uses these with their sold products as features for clustering. The extracted structured information is the foundation for further data exploration, such as identifying the top vendors or fine-granular price analyses. Our evaluation shows that pretrained word vectors perform better for unsupervised clustering than state-of-the-art transformer models, while the latter is still superior for sequence labeling.
We study the novel possibilities computer aided design and production open up for the design of building systems. Such systems today can, via individualized mass production, consist of a larger number and more complex parts than previously and therefore be assembled into more complex wholes. This opens up the possibility of designing specialized systems specifically for single buildings. The common order of starting with a building system and designing a building using this system can be reversed to designing a building first and then developing a system specifically for that building. We present and discuss research that incorporates students design projects into research work and fosters links between research and teaching.
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.
In the RoboCup@Home domestic service robot competition, complex tasks such as "get the cup from the kitchen and bring it to the living room" or "find me this and that object in the apartment" have to be accomplished. At these competitions the robots may only be instructed by natural language. As humans use qualitative concepts such as "near" or "far", the robot needs to cope with them, too. For our domestic robot, we use the robot programming and plan language Readylog, our variant of Golog. In previous work we extended the action language Golog, which was developed for the high-level control of agents and robots, with fuzzy concepts and showed how to embed fuzzy controllers in Golog. In this paper, we demonstrate how these notions can be fruitfully applied to two domestic service robotic scenarios. In the first application, we demonstrate how qualitative fluents based on a fuzzy set semantics can be deployed. In the second program, we show an example of a fuzzy controller for a follow-a-person task.
A Gamified Information System (GIS) implements game concepts and elements, such as affordances and game design principles to motivate people. Based on the idea to develop a GIS to increase the motivation of software developers to perform software quality tasks, the research work at hand aims at investigating relevant requirements from that target group. Therefore, 14 interviews with software development experts are conducted and analyzed. According to the results, software developers prefer the affordances points, narrative storytelling in a multiplayer and a round-based setting. Furthermore, six design principles for the development of a GIS are derived.
Gamification and gamified information systems (GIS) apply video game elements to encourage the work on boring and everyday tasks. Meanwhile, several research works provide evidence that gamification increases efficiency and effectivity of such tasks. The paper at hand investigates the health care sector, which is challenged with cost pressure and suffers in process efficiency. We hypothesize that GIS may improve the efficiency and quality of care processes. By applying an interview-based content analysis, the paper at hand evaluates gamification elements in an assisted living environment and provides three research contributions. First, insights into relevant GIS affordances and application examples for assisted living facilities are given. Second, assisted living experts evaluate GIS design guidelines. Both the relevant affordances and design principles comprise a basis for the development of a GIS for social workers in assisted living facilities. Third, potential adoption barriers and design guidelines for GIS in assisted living are presented.
Industry 4.0 imposes many challenges for manufacturing companies and their employees. Innovative and effective training strategies are required to cope with fast-changing production environments and new manufacturing technologies. Virtual Reality (VR) offers new ways of on-the-job, on-demand, and off-premise training. A novel concept and evaluation system combining Gamification and VR practice for flexible assembly tasks is proposed in this paper and compared to existing works. It is based on directed acyclic graphs and a leveling system. The concept enables a learning speed which is adjustable to the users’ pace and dynamics, while the evaluation system facilitates adaptive work sequences and allows employee-specific task fulfillment. The concept was implemented and analyzed in the Industry 4.0 model factory at FH Aachen for mechanical assembly jobs.
Generating synthetic LiDAR point cloud data for object detection using the Unreal Game Engine
(2024)
Object detection based on artificial intelligence is ubiquitous in today’s computer vision research and application. The training of the neural networks for object detection requires large and high-quality datasets. Besides datasets based on image data, datasets derived from point clouds offer several advantages. However, training datasets are sparse and their generation requires a lot of effort, especially in industrial domains. A solution to this issue offers the generation of synthetic point cloud data. Based on the design science research method, the work at hand proposes an approach and its instantiation for generating synthetic point cloud data based on the Unreal Engine. The point cloud quality is evaluated by comparing the synthetic cloud to a real-world point cloud. Within a practical example the applicability of the Unreal Game engine for synthetic point cloud generation could be successfully demonstrated.
Durch die Fragmentierung von Wertschöpfungsketten ergeben sich neue Herausforderungen für das Management von Kundenbeziehungen. Die Dissertation untersucht die daraus resultierenden Anforderungen an eine übergreifende Integration von Customer Relationship Management in der
Telekommunikationsindustrie. Ziel ist es, durch Anwendung von Methoden eines Enterprise Architecture Framework eine übergreifend Lösung zu gestalten. Grundlegende Prämisse dabei ist, dass die übergreifende Gestaltung eines Customer Relationship Management für alle an der
Wertschöpfung beteiligten Unternehmen vorteilhaft ist.
Low-thrust space propulsion systems enable flexible high-energy deep space missions, but the design and optimization of the interplanetary transfer trajectory is usually difficult. It involves much experience and expert knowledge because the convergence behavior of traditional local trajectory optimization methods depends strongly on an adequate initial guess. Within this extended abstract, evolutionary neurocontrol, a method that fuses artificial neural networks and evolutionary algorithms, is proposed as a smart global method for low-thrust trajectory optimization. It does not require an initial guess. The implementation of evolutionary neurocontrol is detailed and its performance is shown for an exemplary mission.