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Architecture for platform- and hardware-independent mesh networks : how to unify the channels
(2013)
This paper will prove that mesh networks among different platforms and hardware channels can help to channel valuable information even if public telecommunication infrastructure is not available due to arbitrary reasons. Therefore, results of a simulation for mesh networks on mass events will be provided, followed by the developed architecture and an outlook on future research. The developed architecture is currently being implemented and field tested on mass events.
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 understanding that optimized components do not automatically lead to energy-efficient systems sets the attention from the single component on the entire technical system. At TU Darmstadt, a new field of research named Technical Operations Research (TOR) has its origin. It combines mathematical and technical know-how for the optimal design of technical systems. We illustrate our optimization approach in a case study for the design of a ventilation system with the ambition to minimize the energy consumption for a temporal distribution of diverse load demands. By combining scaling laws with our optimization methods we find the optimal combination of fans and show the advantage of the use of multiple fans.
Water distribution systems are an essential supply infrastructure for cities. Given that climatic and demographic influences will pose further challenges for these infrastructures in the future, the resilience of water supply systems, i.e. their ability to withstand and recover from disruptions, has recently become a subject of research. To assess the resilience of a WDS, different graph-theoretical approaches exist. Next to general metrics characterizing the network topology, also hydraulic and technical restrictions have to be taken into account. In this work, the resilience of an exemplary water distribution network of a major German city is assessed, and a Mixed-Integer Program is presented which allows to assess the impact of capacity adaptations on its resilience.
Assessment of RF Safety of Transmit Coils at 7 Tesla by Experimental and Numerical Procedures (490.)
(2012)
Large industrial facilities and power plants often require a huge number fo information and control cables between the differnet structures. These I&C-cables can be routed in reinforced concrete cable ducts or in isolated buried cable runs. KTA 2206 is the German lightning protection standard for nuclear power plants. During the last several years considerable effort has been made to revise this standard. Despite the well established principles and design guidelines for the construction of the lightning protection system, this standard puts special emphasis on the coupling of transient overvoltages to I&C-cables.
This study presents the concept of AstroBioLab, an autonomous astrobiological field laboratory tailored for the exploration of (sub)glacial habitats. AstroBioLab is an integral component of the TRIPLE (Technologies for Rapid Ice Penetration and subglacial Lake Exploration) DLR-funded project, aimed at advancing astrobiology research through the development and deployment of innovative technologies. AstroBioLab integrates diverse measurement techniques such as fluorescence microscopy, DNA sequencing and fluorescence spectrometry, while leveraging microfluidics for efficient sample delivery and preparation.
Attitude and Orbital Dynamics Modeling for an Uncontrolled Solar-Sail Experiment in Low-Earth Orbit
(2015)
Gossamer-1 is the first project of the three-step Gossamer roadmap, the purpose of which is to develop, prove and demonstrate that solar-sail technology is a safe and reliable propulsion technique for long-lasting and high-energy missions. This paper firstly presents the structural analysis performed on the sail to understand its elastic behavior. The results are then used in attitude and orbital simulations. The model considers the main forces and torques that a satellite experiences in low-Earth orbit coupled with the sail deformation. Doing the simulations for varying initial conditions in attitude and rotation rate, the results show initial states to avoid and maximum rotation rates reached for correct and faulty deployment of the sail. Lastly comparisons with the classic flat sail model are carried out to test the hypothesis that the elastic behavior does play a role in the attitude and orbital behavior of the sail
Close interrelations between sound and image are not a mere phenomenon of today’s multimedia technology. The idea of the synthesis of different media lies at the core of the concept of the Gesamtkunstwerk in the second half of the 19th century and it can also be traced back to the synaesthesia debate at the beginning of the 20th century [...].
Having well-defined control strategies for fuel cells, that can efficiently detect errors and take corrective action is critically important for safety in all applications, and especially so in aviation. The algorithms not only ensure operator safety by monitoring the fuel cell and connected components, but also contribute to extending the health of the fuel cell, its durability and safe operation over its lifetime. While sensors are used to provide peripheral data surrounding the fuel cell, the internal states of the fuel cell cannot be directly measured. To overcome this restriction, Kalman Filter has been implemented as an internal state observer.
Other safety conditions are evaluated using real-time data from every connected sensor and corrective actions automatically take place to ensure safety. The algorithms discussed in this paper have been validated thorough Model-in-the-Loop (MiL) tests as well as practical validation at a dedicated test bench.
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.
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.
Reliable methods for automatic readability assessment have the potential to impact a variety of fields, ranging from machine translation to self-informed learning. Recently, large language models for the German language (such as GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep Learning based approaches that promise to further improve automatic readability assessment. In this contribution, we studied the ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences. We combined these models with linguistic features and investigated the dependence of prediction performance on ensemble size and composition. Mixed ensembles of GBERT and GPT-2-Wechsel performed better than ensembles of the same size consisting of only GBERT or GPT-2-Wechsel models. Our models were evaluated in the GermEval 2022 Shared Task on Text Complexity Assessment on data of German sentences. On out-of-sample data, our best ensemble achieved a root mean squared error of 0:435.
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.
Recent earthquakes showed that low-rise URM buildings following codecompliant seismic design and details behaved in general very well without substantial damages. Although advances in simulation tools make nonlinear calculation methods more readily accessible to designers, linear analyses will still be the standard design method for years to come. The present paper aims to improve the linear seismic design method by providing a proper definition of the q-factor of URM buildings. Values of q-factors are derived for low-rise URM buildings with rigid diaphragms, with reference to modern structural configurations realized in low to moderate seismic areas of Italy and Germany. The behaviour factor components for deformation and energy dissipation capacity and for overstrength due to the redistribution of forces are derived by means of pushover analyses. As a result of the investigations, rationally based values of the behaviour factor q to be used in linear analyses in the range of 2.0 to 3.0 are proposed.
The overall objective of this study is to develop a new external fixator, which closely maps the native kinematics of the elbow to decrease the joint force resulting in reduced rehabilitation time and pain. An experimental setup was designed to determine the native kinematics of the elbow during flexion of cadaveric arms. As a preliminary study, data from literature was used to modify a published biomechanical model for the calculation of the joint and muscle forces. They were compared to the original model and the effect of the kinematic refinement was evaluated. Furthermore, the obtained muscle forces were determined in order to apply them in the experimental setup. The joint forces in the modified model differed slightly from the forces in the original model. The muscle force curves changed particularly for small flexion angles but their magnitude for larger angles was consistent.
Biomechanical simulation of different prosthetic meshes for repairing uterine/vaginal vault prolapse
(2017)
Tests with palm tree leaves have just started yet and scan data are in the process to be analyzed. The final goal of future project for palm tree gender and species recognition will be to develop optical scanning technology to be applied to date palm tree leaves for in–situ screening purposes. Depending on the software used and the particular requirements of the users the technology potentially shall be able to identify palm tree diseases, palm tree gender, and species of young date palm trees by scanning leaves.
This paper reports a first microbial biosensor for rapid and cost-effective determination of organophosphorus pesticides fenitrothion and EPN. The biosensor consisted of recombinant PNP-degrading/oxidizing bacteria Pseudomonas putida JS444 anchoring and displaying organophosphorus hydrolase (OPH) on its cell surface as biological sensing element and a dissolved oxygen electrode as the transducer. Surfaceexpressed OPH catalyzed the hydrolysis of fenitrothion and EPN to release 3-methyl-4-nitrophenol and p-nitrophenol, respectively, which were oxidized by the enzymatic machinery of Pseudomonas putida JS444 to carbon dioxide while consuming oxygen, which was measured and correlated to the concentration of organophosphates. Under the optimum operating conditions, the biosensor was able to measure as low as 277 ppb of fenitrothion and 1.6 ppm of EPN without interference from phenolic compounds and other commonly used pesticides such as carbamate pesticides, triazine herbicides and organophosphate pesticides without nitrophenyl substituent. The applicability of the biosensor to lake water was also demonstrated.
In many historical centers in Europe, stone masonry is part of building aggregates, which developed when the layout of the city or village was densified. The analysis of such building aggregates is very challenging and modelling guidelines missing. Advances in the development of analysis methods have been impeded by the lack of experimental data on the seismic response of such aggregates. The SERA project AIMS (Seismic Testing of Adjacent Interacting Masonry Structures) provides such experimental data by testing an aggregate of two buildings under two horizontal components of dynamic excitation. With the aim to advance the modelling of unreinforced masonry aggregates, a blind prediction competition is organized before the experimental campaign. Each group has been provided a complete set of construction drawings, material properties, testing sequence and the list of measurements to be reported. The applied modelling approaches span from equivalent frame models to Finite Element models using shell elements and discrete element models with solid elements. This paper compares the first entries, regarding the modelling approaches, results in terms of base shear, roof displacements, interface openings, and the failure modes.
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.
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.
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.
Characterization and evaluation of lignocellulosic biomass 130 hydrolysates for ABE fermentation
(2016)
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.
Comparative assessment of parallel-hybrid-electric propulsion systems for four different aircraft
(2020)
As battery technologies advance, electric propulsion concepts are on the edge of disrupting aviation markets. However, until electric energy storage systems are ready to allow fully electric aircraft, the combination of combustion engine and electric motor as a hybrid-electric propulsion system seems to be a promising intermediate solution. Consequently, the design space for future aircraft is expanded considerably, as serial-hybrid-, parallel-hybrid-, fully-electric, and conventional propulsion systems must all be considered. While the best propulsion system depends on a multitude of requirements and considerations, trends can be observed for certain types of aircraft and certain types of missions. This paper provides insight into some factors that drive a new design towards either conventional or hybrid propulsion systems. General aviation aircraft, VTOL air taxis, transport aircraft, and UAVs are chosen as case studies. Typical missions for each class are considered, and the aircraft are analyzed regarding their take-off mass and primary energy consumption. For these case studies, a high-level approach is chosen, using an initial sizing methodology. Results indicate that hybrid-electric propulsion systems should be considered if the propulsion system is sized by short-duration power constraints (e.g. take-off, climb). However, if the propulsion system is sized by a continuous power requirement (e.g. cruise), hybrid-electric systems offer hardly any benefit.
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.
The Robot Operating System (ROS) is the current de-facto standard in robot middlewares. The steadily increasing size of the user base results in a greater demand for training as well. User groups range from students in academia to industry professionals with a broad spectrum of developers in between. To deliver high quality training and education to any of these audiences, educators need to tailor individual curricula for any such training. In this paper, we present an approach to ease compiling curricula for ROS trainings based on a taxonomy of the teaching contents. The instructor can select a set of dedicated learning units and the system will automatically compile the teaching material based on the dependencies of the units selected and a set of parameters for a particular training. We walk through an example training to illustrate our work.
Complexity for heterogeneous classes: teaching embedded systems using an open project approach
(2019)
Composite improvement of textile reinforced concrete by polymeric impregnation of the textiles
(2006)
Computational aeroelastic analysis and design of the HIRENASD wind tunnel wing model and tests
(2007)
Research on robotic lunar exploration has seen a broad revival, especially since the Google Lunar X-Prize increasingly brought private endeavors into play. This development is supported by national agencies with the aim of enabling long-term lunar infrastructure for in-situ operations and the establishment of a moon village. One challenge for effective exploration missions is developing a compact and lightweight robotic rover to reduce launch costs and open the possibility for secondary payload options. Existing micro rovers for exploration missions are clearly limited by their design for one day of sunlight and their low level of autonomy. For expanding the potential mission applications and range of use, an extension of lifetime could be reached by surviving the lunar night and providing a higher level of autonomy. To address this objective, the paper presents a system design concept for a lightweight micro rover with long-term mission duration capabilities, derived from a multi-day lunar mission scenario at equatorial regions. Technical solution approaches are described, analyzed, and evaluated, with emphasis put on the harmonization of hardware selection due to a strictly limited budget in dimensions and power.
The downsizing of spark ignition engines in conjunction with turbocharging is considered to be a promising method for reducing CO₂ emissions. Using this concept, FEV has developed a new, highly efficient drivetrain to demonstrate fuel consumption reduction and drivability in a vehicle based on the Ford Focus ST. The newly designed 1.8L turbocharged gasoline engine incorporates infinitely variable intake and outlet control timing and direct fuel injection utilizing piezo injectors centrally located. In addition, this engine uses a prototype FEV engine control system, with software that was developed and adapted entirely by FEV. The vehicle features a 160 kW engine with a maximum mean effective pressure of 22.4 bar and 34 % savings in simulated fuel consumption. During the first stage, a new electrohydraulically actuated hybrid transmission with seven forward gears and one reverse gear and a single dry starting clutch will be integrated. The electric motor of the hybrid is directly connected to the gear set of the transmission. Utilizing the special gear set layout, the electric motor can provide boost during a change of gears, so that there is no interruption in traction. Therefore, the transmission system combines the advantages of a double clutch controlled gear change (gear change without an interruption in traction) with the efficient, cost-effective design of an automated manual transmission system. Additionally, the transmission provides a purely electric drive system and the operation of an air-conditioning compressor during the engine stop phases. One other alternative is through the use of CAI (Controlled Auto Ignition), which incorporates a process developed by FEV for controlled compression ignition.
Applications of Graph Transformations with Industrial Relevance Lecture Notes in Computer Science, 2004, Volume 3062/2004, 434-439, DOI: http://dx.doi.org/10.1007/978-3-540-25959-6_33 This paper gives a brief overview of the tools we have developed to support conceptual design in civil engineering. Based on the UPGRADE framework, two applications, one for the knowledge engineer and another for architects allow to store domain specific knowledge and to use this knowledge during conceptual design. Consistency analyses check the design against the defined knowledge and inform the architect if rules are violated.
In the paper a lightning protection design concept for renewable energy hybrid-systems without power mains connection is described. Based on a risk analysis protection measures against direct strikes and overvoltages are shown in an overview. The design concept is realized exemplarily for the hybrid-system VATALI on the Greek island Crete. VATALI, not lightning protected at that time, was a victim of a lightning strike in the year 2000 causing destructions and damages of some mechanical and electrical components with costs of approx. 60.000 €. The hardware costs for the protection measures were about 15.000 €: about 50% of the costs are due to protection measures against direct strikes, 50% are due to overvoltage protection.
A German–Brazilian research project investigates sugarcane as an energy plant in anaerobic digestion for biogas production. The aim of the project is a continuous, efficient, and stable biogas process with sugarcane as the substrate. Tests are carried out in a fermenter with a volume of 10 l.
In order to optimize the space–time load to achieve a stable process, a continuous process in laboratory scale has been devised. The daily feed in quantity and the harvest time of the substrate sugarcane has been varied. Analyses of the digester content were conducted twice per week to monitor the process: The ratio of inorganic carbon content to volatile organic acid content (VFA/TAC), the concentration of short-chain fatty acids, the organic dry matter, the pH value, and the total nitrogen, phosphate, and ammonium concentrations were monitored. In addition, the gas quality (the percentages of CO₂, CH₄, and H₂) and the quantity of the produced gas were analyzed.
The investigations have exhibited feasible and economical production of biogas in a continuous process with energy cane as substrate. With a daily feeding rate of 1.68gᵥₛ/l*d the average specific gas formation rate was 0.5 m3/kgᵥₛ. The long-term study demonstrates a surprisingly fast metabolism of short-chain fatty acids. This indicates a stable and less susceptible process compared to other substrates.