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Within the framework of the project a genderand diversity-oriented teaching evaluation and modern, media-supported blended learning approaches were used in order to achieve the intended goals. First research results of the literature and status quo analysis were already implemented and tested in newly designed teaching approaches, for example in a multidisciplinary introductory lecture of civil engineering at RWTH Aachen University.
Numerical models have become an essential part of snow avalanche engineering. Recent
advances in understanding the rheology of flowing snow and the mechanics of entrainment and
deposition have made numerical models more reliable. Coupled with field observations and historical
records, they are especially helpful in understanding avalanche flow in complex terrain. However, the
application of numerical models poses several new challenges to avalanche engineers. A detailed
understanding of the avalanche phenomena is required to specify initial conditions (release zone
dimensions and snowcover entrainment rates) as well as the friction parameters, which are no longer
based on empirical back-calculations, rather terrain roughness, vegetation and snow properties. In this
paper we discuss these problems by presenting the computer model RAMMS, which was specially
designed by the SLF as a practical tool for avalanche engineers. RAMMS solves the depth-averaged
equations governing avalanche flow with first and second-order numerical solution schemes. A
tremendous effort has been invested in the implementation of advanced input and output features.
Simulation results are therefore clearly and easily visualized to simplify their interpretation. More
importantly, RAMMS has been applied to a series of well-documented avalanches to gauge model
performance. In this paper we present the governing differential equations, highlight some of the input
and output features of RAMMS and then discuss the simulation of the Gatschiefer avalanche that
occurred in April 2008, near Klosters/Monbiel, Switzerland.
Proceedings of the International Conference on Material Theory and Nonlinear Dynamics. MatDyn. Hanoi, Vietnam, Sept. 24-26, 2007, 8 p. In this paper, a method is introduced to determine the limit load of general shells using the finite element method. The method is based on an upper bound limit and shakedown analysis with elastic-perfectly plastic material model. A non-linear constrained optimisation problem is solved by using Newton’s method in conjunction with a penalty method and the Lagrangean dual method. Numerical investigation of a pipe bend subjected to bending moments proves the effectiveness of the algorithm.
[Paper of the X International Symposium on Lightning Protection 9th - 13th November, 2009 - Curitiba, Brazil. 6 pages] The international standard IEC 62305-3, published in 2006, requires as an integral part of the lightning protection system (LPS) the consideration of a separation distance between the conductors of the LPS and metal and electrical installations inside the structure to be protected. IEC 62305-3 gives two different methods for this calculation: a standard, simplified approach and a more detailed approach, which differ especially regarding the treatment of the current sharing effect on the LPS conductors. Hence, different results for the separation distance are possible, leading to some discrepancies in the use of the standard. The standard approach defined in the main part (Clause 6.3) and in Annex C of the standard in some cases may lead to a severe oversizing of the required separation distance. The detailed approach described in Annex E naturally gives more correct results. However, a calculation of the current sharing amongst all parts of the air-termination and downconductor network is necessary, in many cases requiring the use of network analysis programs. In this paper simplified methods for the assessment of the current sharing are presented, which are easy to use as well as sufficiently adequate.
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.
Characterization and evaluation of lignocellulosic biomass 130 hydrolysates for ABE fermentation
(2016)
Chemische Sensoren mit Bariumstrontiumtitanat als funktionelle Schicht zur Multiparameterdetektion
(2013)
In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway.
Kawasaki Heavy Industries, Ltd. (KHI), Aachen University of Applied Sciences, and B&B-AGEMA GmbH have investigated the potential of low NOx micro-mix (MMX) hydrogen combustion and its application to an industrial gas turbine combustor. Engine demonstration tests of a MMX combustor for the M1A-17 gas turbine with a co-generation system were conducted in the hydrogen-fueled power generation plant in Kobe City, Japan.
This paper presents the results of the commissioning test and the combined heat and power (CHP) supply demonstration. In the commissioning test, grid interconnection, loading tests and load cut-off tests were successfully conducted. All measurement results satisfied the Japanese environmental regulation values. Dust and soot as well as SOx were not detected. The NOx emissions were below 84 ppmv at 15 % O2. The noise level at the site boundary was below 60 dB. The vibration at the site boundary was below 45 dB.
During the combined heat and power supply demonstration, heat and power were supplied to neighboring public facilities with the MMX combustion technology and 100 % hydrogen fuel. The electric power output reached 1800 kW at which the NOx emissions were 72 ppmv at 15 % O2, and 60 %RH. Combustion instabilities were not observed. The gas turbine efficiency was improved by about 1 % compared to a non-premixed type combustor with water injection as NOx reduction method. During a total equivalent operation time of 1040 hours, all combustor parts, the M1A-17 gas turbine as such, and the co-generation system were without any issues.
Kawasaki Heavy Industries, LTD. (KHI) has research and development projects for a future hydrogen society. These projects comprise the complete hydrogen cycle, including the production of hydrogen gas, the refinement and liquefaction for transportation and storage, and finally the utilization in a gas turbine for electricity and heat supply. Within the development of the hydrogen gas turbine, the key technology is stable and low NOx hydrogen combustion, namely the Dry Low NOx (DLN) hydrogen combustion.
KHI, Aachen University of Applied Science, and B&B-AGEMA have investigated the possibility of low NOx micro-mix hydrogen combustion and its application to an industrial gas turbine combustor. From 2014 to 2018, KHI developed a DLN hydrogen combustor for a 2MW class industrial gas turbine with the micro-mix technology. Thereby, the ignition performance, the flame stability for equivalent rotational speed, and higher load conditions were investigated. NOx emission values were kept about half of the Air Pollution Control Law in Japan: 84ppm (O2-15%). Hereby, the elementary combustor development was completed.
From May 2020, KHI started the engine demonstration operation by using an M1A-17 gas turbine with a co-generation system located in the hydrogen-fueled power generation plant in Kobe City, Japan. During the first engine demonstration tests, adjustments of engine starting and load control with fuel staging were investigated. On 21st May, the electrical power output reached 1,635 kW, which corresponds to 100% load (ambient temperature 20 °C), and thereby NOx emissions of 65 ppm (O2-15, 60 RH%) were verified. Here, for the first time, a DLN hydrogen-fueled gas turbine successfully generated power and heat.
O Perfil Reiss apresenta no resultado uma visão da estrutura dos motivos e impulsos de uma pessoa. O ponto de saída para a análise do resultado é o conhecimento de que os motivos vitais (= valores e metas de sobrevivência) formam a moldura, na qual as competências e capacidades que uma pessoa possui possam se desabrochar de forma ideal. A partir desse princípio, não existe no Perfil Reiss nenhum resultado que se possa classificar como correto ou falso, nem como bom ou mau. Uma comparação entre o resultado do Perfil Reiss e o conteúdo de uma atividade apresentada ou ambicionada dá informação sobre até que ponto a capacidade de rendimento de uma pessoa nessa posição pode vir ou virá a se desenvolver por um longo tempo.
Comparative assessment of parallel-hybrid-electric propulsion systems for four different aircraft
(2020)
This paper compares several blade element theory (BET) method-based propeller simulation tools, including an evaluation against static propeller ground tests and high-fidelity Reynolds-Average Navier Stokes (RANS) simulations. Two proprietary propeller geometries for paraglider applications are analysed in static and flight conditions. The RANS simulations are validated with the static test data and used as a reference for comparing the BET in flight conditions. The comparison includes the analysis of varying 2D aerodynamic airfoil parameters and different induced velocity calculation methods. The evaluation of the BET propeller simulation tools shows the strength of the BET tools compared to RANS simulations. The RANS simulations underpredict static experimental data within 10% relative error, while appropriate BET tools overpredict the RANS results by 15–20% relative error. A variation in 2D aerodynamic data depicts the need for highly accurate 2D data for accurate BET results. The nonlinear BET coupled with XFOIL for the 2D aerodynamic data matches best with RANS in static operation and flight conditions. The novel BET tool PropCODE combines both approaches and offers further correction models for highly accurate static and flight condition results.
To maximize the travel distances of battery electric vehicles such as cars or buses for a given amount of stored energy, their powertrains are optimized energetically. One key part within optimization models for electric powertrains is the efficiency map of the electric motor. The underlying function is usually highly nonlinear and nonconvex and leads to major challenges within a global optimization process. To enable faster solution times, one possibility is the usage of piecewise linearization techniques to approximate the nonlinear efficiency map with linear constraints. Therefore, we evaluate the influence of different piecewise linearization modeling techniques on the overall solution process and compare the solution time and accuracy for methods with and without explicitly used binary variables.