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Previous studies of the hyphenation of gas chromatographic separation and spectrophotometric detection in the ultraviolet wavelength range between 168 and 330 nm showed a high potential for applications where the analysis of complex samples is required. Within this paper the development of a state-of-the-art detection system for compounds in the vapour phase is described, offering an improved behaviour compared to previous systems: Dependent on the requirements of established detection systems hyphenated with gas chromatography, the main components of the system have to be designed for optimum performance and reliability of the spectrophotometric detector: A deuterium lamp as a broadband light source has been selected for improved stability in the measurements. A new-type absorption cell based on fiber-optics has been developed considering the dynamic necessary to compete with existing techniques. In addition, the influence of the volume of the cell on the chromatogram needs to be analyzed. Tests for determining the performance of the absorption cell in terms of chemical and thermal influences have been carried out. A new spectrophotometer with adequate spectral resolution in the wavelength range, offering improved stability and dynamic for an efficient use in this application was developed. Furthermore, the influence of each component on the performance, reliability and stability of the sensor system will be discussed. An overview and outlook over the potential applications in the environmental, scientific and medical field will be given.
In thin-layer chromatography, fiber-bundle arrays have been introduced for spectral absorption measurements in the UV-region. Using all-silica fiber bundles, the exciting light will be detected after re-emission on the plate with a fiberoptic spectrometer. In addition, fluorescence light can be detected which will be masked by the re-emitted light. Therefore, it is helpful to separate the absorption and fluorescence on the TLC-plate. A modified three-array assembly has been developed: using one array for detection, the two others are used for excitation with broadband band deuterium-light and with UV-LEDs adjusted to the substances under test. As an example, the quantification of glucosamine in nutritional supplements or spinach leaf extract will be described. Using simply heating of the amino-plate for derivation, the reaction product of Glucosamine can be detected sensitively either by light absorption or by fluorescence, using the new fiber-optic assembly. In addition, the properties of the new 3-row fiber-optic array and the commercially available UV-LEDs will be shown, in the interesting wavelength region for excitation of fluorescence, from 260 nm to 360 nm. The squint angle having an influence on coupling efficiency and spatial resolution will be measured with the inverse farfield method. Some properties of UV-LEDs for analytical applications will be described and discussed, too.
Variable refrigerant flow (VRF) and variable air volume (VAV) systems are considered among the best heating, ventilation, and air conditioning systems (HVAC) thanks to their ability to provide cooling and heating in different thermal zones of the same building. As well as their ability to recover the heat rejected from spaces requiring cooling and reuse it to heat another space. Nevertheless, at the same time, these systems are considered one of the most energy-consuming systems in the building. So, it is crucial to well size the system according to the building’s cooling and heating needs and the indoor temperature fluctuations. This study aims to compare these two energy systems by conducting an energy model simulation of a real building under a semi-arid climate for cooling and heating periods. The developed building energy model (BEM) was validated and calibrated using measured and simulated indoor air temperature and energy consumption data. The study aims to evaluate the effect of these HVAC systems on energy consumption and the indoor thermal comfort of the building. The numerical model was based on the Energy Plus simulation engine. The approach used in this paper has allowed us to reach significant quantitative energy saving along with a high level of indoor thermal comfort by using the VRF system compared to the VAV system. The findings prove that the VRF system provides 46.18% of the annual total heating energy savings and 6.14% of the annual cooling and ventilation energy savings compared to the VAV system.
Polyarticulated active prostheses constitute a promising solution for upper limb amputees. The bottleneck for their adoption though, is the lack of intuitive control. In this context, machine learning algorithms based on pattern recognition from electromyographic (EMG) signals represent a great opportunity for naturally operating prosthetic devices, but their performance is strongly affected by the selection of input features. In this study, we investigated different combinations of 13 EMG-derived features obtained from EMG signals of healthy individuals performing upper limb movements and tested their performance for movement classification using an Artificial Neural Network. We found that input data (i.e., the set of input features) can be reduced by more than 50% without any loss in accuracy, while diminishing the computing time required to train the classifier. Our results indicate that input features must be properly selected in order to optimize prosthetic control.
The increasing diffusion of rapidly developing AI technologies led to the idea of the experiment to combine TRIZ-based automated idea generation with the natural language processing tool ChatGPT, using the chatbot to interpret the automatically generated elementary solution principles. The article explores the opportunities and benefits of a novel AI-enhanced approach to teaching systematic innovation, analyses the learning experience, identifies the factors that affect students' innovation and problem-solving performance, and highlights the main difficulties students face, especially in interdisciplinary problems.
A smart energy concept was designed and implemented for a cluster of 5 existing multi-family houses, which combines heat pumps, photovoltaic (PV) modules and combined heat and power units (CHP) to achieve energy- and cost-efficient operation. Measurement results of the first year of operation show that the local power generation by PV modules and CHP unit has a positive effect on the electrical self-sufficiency by reducing electricity import from the grid. In winter, when the CHP unit operates continuously for long periods, the entire electricity for the heat pump and 91 % of the total electricity demand of the neighborhood are supplied locally. In summer, only 53 % is generated within the neighborhood. The use of a specifically developed energy management system EMS is intended to further increase this share. CO2 emissions for heating and electricity of the neighborhood are evaluated and amount to 18.4 kg/(m2a). Compared to the previous energy system consisting of gas boilers (29.1 kg/(m2a)), savings of 37 % are achieved with electricity consumption from the grid being reduced by 65 %. In the second construction stage, an additional heat pump, CHP unit and PV modules will be added. The measurement results indicate that the final district energy system is likely to achieve the ambitious CO2 reduction goal of -50% and further increase the self-sufficiency of the district.
Enhancing engineering creativity with automated formulation of elementary solution principles
(2023)
The paper describes a method for the automated formulation of elementary creative stimuli for product or process design at different levels of abstraction and in different engineering domains. The experimental study evaluates the impact of structured automated idea generation on inventive thinking in engineering design and compares it with previous experimental studies in educational and industrial settings. The outlook highlights the benefits of using automated ideation in the context of AI-assisted invention and innovation.
This book constitutes the proceedings of the 23rd International TRIZ Future Conference on Towards AI-Aided Invention and Innovation, TFC 2023, which was held in Offenburg, Germany, during September 12–14, 2023. The event was sponsored by IFIP WG 5.4.
The 43 full papers presented in this book were carefully reviewed and selected from 80 submissions. The papers are divided into the following topical sections: AI and TRIZ; sustainable development; general vision of TRIZ; TRIZ impact in society; and TRIZ case studies.
Eco-innovations in chemical processes should be designed to use raw materials, energy and water as efficiently and economically as possible to avoid the generation of hazardous waste and to conserve raw material reserves. Applying inventive principles identified in natural systems to chemical process design can help avoid secondary problems. However, the selection of nature-inspired principles to improve technological or environmental problems is very time-consuming. In addition, it is necessary to match the strongest principles with the problems to be solved. Therefore, the research paper proposes a classification and assignment of nature-inspired inventive principles to eco-parameters, eco-engineering contradictions and eco-innovation domains, taking into account environmental, technological and economic requirements. This classification will help to identify suitable principles quickly and also to realize rapid innovation. In addition, to validate the proposed classification approach, the study is illustrated with the application of nature-inspired invention principles for the development of a sustainable process design for the extraction of high-purity silicon dioxide from pyrophyllite ores. Finally, the paper defines a future research agenda in the field of nature-inspired eco-engineering in the context of AI-assisted invention and innovation.
Encapsulant-free N.I.C.E. modules have strong ecological advantages compared to conventional laminated modules but suffer generally from lower electrical performance. Via long-term outdoor monitoring of fullsize industrial modules of both types with identical solar cells, we investigated if the performance difference remains constant over time and which parameters influence its value. After assessing about a full year’s data, two obvious levers for N.I.C.E. optimization are identified: The usage of textured glass and transparent adhesives on the module rear side. Also, the performance loss could be alleviated using tracking systems due to lower AOI values. Our measurements show additionally that N.I.C.E. module surfaces are in average about 2.5°C cooler compared to laminated modules. With these findings, we lay out a roadmap to reduce today’s LIV gap of about 5%rel by different optimizations.
In this paper, we propose an approach for gait phase detection for flat and inclined surfaces that can be used for an ankle-foot orthosis and the humanoid robot Sweaty. To cover different use cases, we use a rule-based algorithm. This offers the required flexibility and real-time capability. The inputs of the algorithm are inertial measurement unit and ankle joint angle signals. We show that the gait phases with the orthosis worn by a human participant and with Sweaty are reliably recognized by the algorithm under the condition of adapted transition conditions. E.g., the specificity for human gait on flat surfaces is 92 %. For the robot Sweaty, 95 % results in fully recognized gait cycles. Furthermore, the algorithm also allows the determination of the inclination angle of the ramp. The sensors of the orthosis provide 6.9 and that of the robot Sweaty 7.7 when walking onto the reference ramp with slope angle 7.9.
In this paper we report on further success of our work to develop a multi-method energy optimization which works with a digital twin concept. The twin concept serves to replicate production processes of different kinds of production companies, including complex energy systems and test market interactions to then use them for model predictive optimizing. The presented work finally reports about the performed flexibility assessment leading to a flexibility audit with a list of measures and the impact of energy optimizations made related to interactions with the local power grid i.e., the exchange node of the low voltage distribution grid. The analysis and continuous exploration of flexibilities as well as the exchange with energy markets require a “guide” leading to continuous optimization with a further tool like the Flexibility Survey and Control Panel helping decision-making processes on the day-ahead horizon for real production plants or the investment planning to improve machinery, staff schedules and production
infrastructure.
Turbocharger housings in internal combustion engines are subjected to severe mechanical and thermal cyclic loads throughout their life-time or during engine testing. The combination of thermal transients and mechanical load cycling results in a complex evolution of damage, leading to thermo-mechanical fatigue (TMF) of the material. For the computational TMF life assessment of high temperature components, the DTMF model can provide reliable TMF life predictions. The model is based on a short fatigue crack growth law and uses local finite-element (FE) results to predict the number of cycles to failure for a technical crack. In engine applications, it is nowadays often acceptable to have short cracks as long as they do not propagate and cause loss of function of the component. Thus, it is necessary to predict not only potential crack locations and the corresponding number of cycles for a technical crack, but also to determine subsequent crack growth or even a possible crack arrest. In this work, a method is proposed that allows the simulation of TMF crack growth in high temperature components using FE simulations and non-linear fracture mechanics (NLFM).
A NLFM based crack growth simulation method is described. This method starts with the FE analysis of a component. In this paper, the method is demonstrated for an automotive turbocharger housing subjected to TMF loading. A transient elastic-viscoplastic FE analysis is used to simulate four heating and cooling cycles of an engine test. The stresses, inelastic strains, and temperature histories from the FEA are then used to perform TMF life predictions using the standard DTMF model. The crack position and the crack plane of critical hotspots are then identified. Simulated cracks are inserted at the hotspots. For the model demonstrated, cracks were inserted at two hotspot locations. The ΔJ integral is computed as a fracture mechanics parameter at each point along the crack-front, and the crack extension of each point is then evaluated, allowing the crack to grow iteratively. The paper concludes with a comparison of the crack growth curves for both hotspots with experimental results.
Cast aluminum cylinder blocks are frequently used in gasoline and diesel internal combustion engines because of their light-weight advantage. However, the disadvantage of aluminum alloys is their relatively low strength and fatigue resistance which make aluminum blocks prone to fatigue cracking. Engine blocks must withstand a combination of low-cycle fatigue (LCF) thermal loads and high-cycle fatigue (HCF) combustion and dynamic loads. Reliable computational methods are needed that allow for accurate fatigue assessment of cylinder blocks under this combined loading. In several publications, the mechanism-based thermomechanical fatigue (TMF) damage model DTMF describing the growth of short fatigue cracks has been extended to include the effect of both LCF thermal loads and superimposed HCF loadings. This approach is applied to the finite life fatigue assessment of an aluminum cylinder block. The required material properties related to LCF are determined from uniaxial LCF tests. The additional material properties required for the assessment of superimposed HCF are obtained from the literature for similar materials. The predictions of the model agree well with engine dyno test results. Finally, some improvements to the current process are discussed.
To improve the building’s energy efficiency many parameters should be assessed considering the building envelope, energy loads, occupation, and HVAC systems. Fenestration is among the most important variables impacting residential building indoor temperatures. So, it is crucial to use the most optimal energy-efficient window glazing in buildings to reduce energy consumption and at the same time provide visual daylight comfort and thermal comfort. Many studies have focused on the improvement of building energy efficiency focusing on the building envelope or the heating, ventilation, and cooling systems. But just a few studies have focused on studying the effect of glazing on building energy consumption. Thus, this paper aims to study the influence of different glazing types on the building’s heating and cooling energy consumption. A real case study building located under a semi-arid climate was used. The building energy model has been conducted using the OpenStudio simulation engine. Building indoor temperature was calibrated using ASHRAE’s statistical indices. Then a comparative analysis was conducted using seven different types of windows including single, double, and triple glazing filled with air and argon. Tripleglazed and double-glazed windows with argon space offer 37% and 32% of annual energy savings. It should be stressed that the methodology developed in this paper could be useful for further studies to improve building energy efficiency using optimal window glazing.
The variable refrigerant flow system is one of the best heating, ventilation, and air conditioning systems (HVAC) thanks to its ability to provide thermal comfort inside buildings. But, at the same time, these systems are considered one of the most energy-consuming systems in the building sector. Thus, it is crucial to well size the system according to the building’s cooling and heating needs and the indoor temperature fluctuations. Although many researchers have studied the optimization of the building energy performance considering heating or cooling needs, using air handling units, radiant floor heating, and direct expansion valves, few studies have considered the use of multi-objective optimization using only the thermostat setpoints of VRF systems for both cooling and heating needs. Thus, the main aim of this study is to conduct a sensitivity analysis and a multi-objective optimization strategy for a residential building containing a variable refrigerant flow system, to evaluate the effect of the building performance on energy consumption and improve the building energy efficiency. The numerical model was based on the EnergyPlus, jEPlus, and jEPlus+EA simulation engines. The approach used in this paper has allowed us to reach significant quantitative energy saving by varying the cooling and heating setpoints and scheduling scenarios. It should be stressed that this approach could be applied to several HVAC systems to reduce energy-building consumption.
In 4D printing an additively manufactured component is given the ability to change its shape or function under the influence of an external stimulus. To achieve this, special smart materials are used that are able to react to external stimuli in a specific way. So far, a number of different stimuli have already been investigated and initial applications have been impressively demonstrated, such as self-folding bodies and simple grippers. However, a methodical specification for the selection of the stimuli and their implementation was not yet in the foreground of the development.
The focus of this work is therefore to develop a methodical approach with which the technology of 4DP can be used in a solution- and application-oriented manner. The developed approach is based on the conventional design methodology for product development to solve given problems in a structured way. This method is extended by specific approaches under consideration of the 4D printing and smart materials.
To illustrate the developed method, it is implemented in practice using a problem definition in the form of an application example. In this example, which represents the recovery of an object from a difficult-to-access environment, the individual functions of positioning, gripping and extraction are implemented using 4D printing. The material extrusion process is used for additive manufacturing of all components of the example. Finally, the functions are successfully tested. The developed approach offers an innovative and methodical approach to systematically solve technical complex problems using 4DP and smart materials.
4D printing (4DP) is an evolutionary step of 3D printing, which includes the fourth dimension, in this case the time. In different time steps the printed structure shows different shapes, influenced by external stimuli like light, temperature, pH value, electric or magnetic field. The advantage of 4DP is the solution of technical problems without the need for complex internal energy supply via cables or pipes. Previous approaches to 4D printing with magnetoresponsive materials only use materials with limited usability (e.g. hydrogels) and complex programming during the manufacturing process (e.g. using magnets on the nozzle). The 4D printing using unmagnetized particles and the later magnetization allows the use of a standard 3D printer and has the advantage of being easily reproducible and relatively inexpensive for further application. Therefore, a magnetoresponsive feedstock filament is produced which shows elastic and magnetic properties. In a first step, pellets are produced by compounding polymer with magnetic particles. In a second step, those pellets are extruded in form of filament. This filament is printed using a conventional printing system for Material Extrusion (MEX-TRB/P). Various prototypes have been printed, deformed and magnetized, which is called programming. In comparison to shape memory polymers (SMP) the repeatability of the movement is better. The results show the possibilities of application and function of magnetoresponsive materials. In addition, an understanding of the behaviour of this novel material is achieved.
The title expresses goals the Kansas Geological Survey (KGS) has been working toward for some time. This report extends concepts and objectives developed while working on an earlier effort for effective interactive digital maps on the Internet. That work was reported to the 1998 DMT Workshop in Champaign, Illinois (Ross, 1998). The current project goes beyond previous efforts that focused on methods for serving the contents of a geographic information system (GIS); the points, lines, and polygons representing features of the digital geologic map and the data in the attribute tables of the GIS describing those features.
Vortex breakdown phenomena in rotating fluids are investigated both theoretically and experimentally. The fluid is contained in a cone between two spherical surfaces. The primary swirling motion is induced ba the rotating lower boundary. The upper surface can be fixed with non-slip condition or can be a stress-free surface. Depending on these boundary conditions and on the Reynolds number, novel structures of recirculation zones are realized. The axisymmetric flow patterns are simulated numerically by a finite difference method. Experiments are done to visualize the topological structure of the flow pattern and to observe the existence ranges of the different recirculating flows. The comparison between theory and experiment shows good agreement with respect to the topological structure of the flow.
We will present the first example of a two-dimensional scanned TLC-plate, measured by use of a diode-array scanner. A spatial resolution of 250 µm was achieved on plate. The system provides real 2D fluorescence and absorption spectra in the wavelength-range from 190 to 1000 nm with a spectral resolution of greater than 1 nm. A mixture of 12 sulphonamides was separated by using a cyanopropyl-coated silica gel plate (Merck, 1.16464) with the solvent mix of methyl tert-butyl ether-methanol-dichloromethane-cyclohexane-NH3 (25%) (48:2:2:1:1, v/v) in the first and with a mixture of water-acetonitrile-dioxane-ethanol (8:2:1:1, v/v) in the second direction. Both developments were carried out over a distance of 70 mm. A separation number (spot capacity) of 259 was calculated. We discussed a new formula for its calculation in 2D-TLC separations. The drawback of this method is that measuring a 2D-TLC plate needs more than 3 h measurement time.
In this paper a practical way for fatigue life prediction of rubber products under multiaxial loads is shown. This is done by means of fracture mechanical concepts and the energy release rate as the failure criterion. Due to a FEA post-processor the potential energy release rate might be calculated at every material point supposed there was a crack. And therefore the risk of failure and with the help of a strain number curve the time to fatigue is able to be calculated by FEA. This concept is applied for an estimation of the life time of a test specimen with tensile loading from fatigue data of a shear loaded specimen of different design. This rather more theoretical concept of the energy release rate is complemented by experimental crack growth data by a Tear Fatigue Analyzer with its great advantage of reduction of testing time and costs compared to those of fatigue tests. For some materials a thorough characterization of crack growth and fatigue behavior is presented and is applied to estimate the time to fatigue by FEA for a real component under multiaxial loads.
The authors present an abiotically catalyzed glucose fuel cell and demonstrate its application as energy harvesting power source for a cardiac pacemaker. This is enabled by an optimized DC-DC converter operating at 40 % conversion efficiency, which surpasses commercial low-power DC-DC converters. The required fuel cell surface area can thus be reduced from about 125 cm2 to 18 cm2, which would allow for its direct integration onto the pacemaker casing.
Today, thermoforming moulds are mostly produced using conventional mould-building technologies (e.g. milling and drilling) and are made of metal (e.g. aluminium or steel) or hardwood. The tools thus produced are very robust, but are only cost-effective in mass production. For the production of small batches of thermoformed parts, there is a need for moulds which can be produced quickly and economically. A new approach which significantly reduces the production time and cost is the 3D printing process (3DP). The use of this technology to produce thermoforming moulds offers many new options in the geometries which can be manufactured, and in manufacturing time and costs. In a case study of a thermoformed part (a scaled automotive model), the pre-processing of the CAD model of a mould is demonstrated. The mould can be printed within a few hours, and is sufficiently heat-resistant for moulding processes. The important advantages of moulds printed in 3D, in comparison to moulds built using conventional technologies, are the ability to create any shape of channels for the vacuum and the simplification in the production of tool mock-ups. This paper also discusses the economics of the technique, such as a comparison of material costs and manufacturing costs in relation to conventional production technologies and materials.
Digital libraries are providing an increasing amount of data, which is normally structured in a classical way by documents and described by metadata as keywords. The data, even in scientific systems such as digital libraries and virtual research environments, will contain a great amount of noise or information unnecessary for our personal interests. Although there has been a lot of progress in the field of information retrieval, search techniques and other content finding methods, there is still much to be done in the field of information retrieval based on user behavior. This paper presents an approach deployed in the Humboldt Digital Library (HDL) to facilitate the retrieval of relevant information to the users of the system, making recommendations of paragraphs based on their profile and the behavior of other users who share similar profiles. The Humboldt digital library represents an innovative system of open access to the legacy of Alexander von Humboldt in a digital form on the Internet (www.avhumboldt.net). It contributes to the key question, how to present interconnected data in a proper form using information technologies.
This article sets the focus on methods of information technology in the Humboldt Portal, which represents an ongoing research project to develop a virtual research environment on the Internet for the legacy of Alexander von Humboldt. Based on the experiences of developing and providing the Humboldt Digital Library (www.avhumboldt.net) for more than a decade, we defined a working plan to create an Internet portal for comprehensive access to Humboldt’s writings, no matter if documents are provided as PDF files, scan images or XML-TEI documents on external archives (Google Books, Internet Archive, Deutsches Textarchiv, Bibliotheque National de France). Going far beyond services of a digital library we will provide an information network with multimedia assets, which are containing objects like terms, paragraphs, data tables, scan images, or illustrations, together with correlated properties like thematic linkage to other objects, relevant keywords with optional synonyms and dynamic hyperlinks to related translations in different languages. So the Humboldt Portal can contribute to the key question, how to present interconnected data in an appropriate form using information technologies on the Web.
Alexander von Humboldt, a German scientist and explorer of the 19th century, viewed the natural world holistically and described the harmony of nature among the diversity of the physical world as a conjoining between all physical disciplines. He noted in his diary: “Everything is interconnectedness.”
The main feature of Humboldt’s pioneering work was later named “Humboldtian science”, meaning the accurate study of interconnected real phenomena in order to find a definite law and a dynamic cause.
Following Humboldt's idea of nature, an Internet edition of his works must preserve the author’s original intention, retain an awareness of all relevant works, and still adhere to the requirements of scholarly edition.
At the present time, however, the highly unconventional form of his publications has undermined the awareness and a comprehensive study of Humboldt’s works.
Digital libraries should supply dynamic links to sources, maps, images, graphs and relevant texts. New forms of interaction and synthesis between humanistic texts and scientific observation need to be created.
Information technology is the only way to do justice to the broad range of visions, descriptions and the idea of nature of Humboldt’s legacy. It finally leads to virtual research environments as an adequate concept to redesign our digital archives, not only for Humboldt’s documents, but for all interconnected data.
Technology and computer applications influence our daily lives and questions arise concerning the role of artificial intelligence and decision-making algorithms. There are warning voices, that computers can, in theory, emulate human intelligence-and exceed it. This paper points out that a replacement of humans by computers is unlikely, because human thinking is characterized by cognitive heuristics and emotions, which cannot simply be implemented in machines operating with algorithms, procedural data processing or artificial neural networks. However, we are going to share our responsibilities with superior computer systems, which are tracking and surveying all of our digital activities, whereas we have no idea of the decision-making processes inside the machines. It is shown that we need a new digital humanism defining rules of computer responsibilities to avoid digital totalism and comprehensive monitoring and controlling of individuals within the planet Earth.
Autonomous humanoid robots require light weight, high torque and high speed actuators to be able to walk and run. For conventional gears with a fixed gear ratio the product of torque and velocity is constant. On the other hand desired motions require maximum torque and speed. In this paper it is shown that with a variable gear ratio it is possible to vary the relation between torque and velocity. This is achieved by introducing systems of rods and levers to move the joints of our humanoid robot ”Sweaty II”. On the basis of a variable gear ratio low speed and high torque can be achieved for those joint angles, which require this motion mode, whereas high speed and low torque can be realized for those joint angles, where it is favorable for the desired motion.
The series of conferences on Environmental Best Practices (EBP) was inaugurated at the University of Warmia and Mazury in Olsztyn, Poland in 2006 and continued at the Jagiellonian University in Kraków, Poland in 2009. This year the University of Applied Sciences Offenburg produly hosted the third event (EPB3).
Sweaty has already participated several times in RoboCup soccer competitions (Adult Size). Now the work is focused coordinating the play of two robots. Moreover, we are working on stabilizing the gait by adding additional sensor information. An ongoing work is the optimization of the control strategy by balancing between impedance and position control. By minimizing the jerk, gait and overall gameplay should improve significantly.
Sweaty has already participated several times in RoboCup soccer competitions (Adult Size). Now the work is focused on stabilizing the gait. Moreover, we would like to overcome the constraints of a ZMP-algorithm that has a horizontal footplate as precondition for the simplification of the equations. In addition we would like to switch between impedance and position control with a fuzzy-like algorithm that might help to minimize jerks when Sweaty’s feet touch the ground.
Sweaty has already participated four times in RoboCup soccer competitions (Adult Size) and came second three times. While 2016 Sweaty needed a lot of luck to be finalist, 2017 Sweaty was a serious adversary in the preliminary rounds. In 2018 Sweaty showed up in the final with some lack of experience and room for improvements, but not without any chance. This paper describes the intended improvements of the humanoid adult size robot Sweaty in order to qualify for the RoboCup 2019 adult size competition.
This paper describes the Sweaty II humanoid adult size robot trying to qualify for the RoboCup 2018 adult size humanoid competition. Sweaty came 2nd in RoboCup 2017 adult size league. The main characteristics of Sweaty are described in the Team Description Paper 2017. The improvements that have been made or are planned to be implemented for RoboCup 2018 are described in this paper.
This paper describes the Sweaty II humanoid adult size robot trying to qualify for the RoboCup 2017 adult size humanoid competition. Sweaty came 2nd in RoboCup 2016 adult size league. The paper describes the main characteristics of Sweaty that made this success possible, and improvements that have been made or are planned to be implemented for RoboCup 2017.
This paper describes the new Sweaty II humanoid adult size robot trying to qualify for the RoboCup 2016 adult size humanoid competition. Based on experiences during RoboCup 2014, the Sweaty robot has been completely redesigned to a new robot Sweaty II. A major change is the use of linear actuators for the legs. Another characteristic is its indirect actuation by means of rods. This allows a variable transmission ratio depending on the angle of a joint.
This paper describes the new Sweaty humanoid adult size robot trying to qualify for the RoboCup 2014 adult size humanoid competition. The robot is built from scratch to eventually allow it to run. One characteristic is that to prevent the motors from overheating, water evaporation is used for cooling. The robot is literally sweating which has given it its name. Another characteristic is, that the motors are not directly connected to the frame but by means of beams. This allows a variable transmission ratio depending on the angle.
Time Resolved Measurements of Soot Concentrations and Mean Particle Sizes during EUDC and ECE Cycles
(2002)
A system for the on-line/in-line measurement of soot particle sizes and concentrations in the undiluted exhaust gas of diesel engines was developed and successfully tested. The unit uses the individual attenuations of three different laser wavelengths and is combined with an optical cell (white principle) with adjustable path lengths from 2.5 to 15 meters.
More than 200 years ago, the scientist Alexander von Humboldt noted in his travel diaries that "everything is interconnectedness", when he was fascinated by nature and the phenomena observed. The view of nature has become much more detailed through the knowledge of phenomena and natural processes, which led to a more precise view of nature shaped by Humboldt. Technological progress and the artificial intelligence of highly developed computer systems are upsetting this view and changing the established world view through a new, unprecedented interaction between man and machinery. Thus we need digital axioms and comprehensive rules and laws for such autonomous acting systems that determine human interaction between cybernetic systems and biological individuals. This digital humanism should encompass our relationship to nature, our handling of the complexity and diversity of nature and the technological influences on society in order to avoid technical colonialism through supercomputers.
Hot forging dies are subjected to high cyclic thermo-mechanical loads. In critical areas, the occurring stresses can exceed the material’s yield limit. Additionally, loading at high temperatures leads to thermal softening of the used martensitic materials. These effects can result in an early crack initiation and unexpected failure of the dies, usually described as thermo-mechanical fatigue (TMF). In previous works, a temperature-dependent cyclic plasticity model for the martensitic hot forging tool steel 1.2367 (X38CrMoV5-3) was developed and implemented in the finite element (FE)-software Abaqus. However, in the forging industry, application-specific software is usually used to ensure cost-efficient numerical process design. Therefore, a new implementation for the FE-software Simufact Forming 16.0 is presented in this work. The results are compared and validated with the original implementation by means of a numerical compression test and a cyclic simulation is calculated with Simufact Forming.
Significant improvements in module performance are possible via implementation of multi-wire electrodes. This is economically sound as long as the mechanical yield of the production is maintained. While flat ribbons have a relatively large contact area to exert forces onto the solar cell, wires with round cross section reduce this contact area considerably – in theory to an infinitively thin line. Therefore, the local stresses induced by the electrodes might increase to a point that mechanical production yields suffer unacceptably.
In this paper, we assess this issue by an analytical mechanical model as well as experiments with an encapsulant-free N.I.C.E. test setup. From these, we can derive estimations for the relationship between lay-up accuracy and expected breakage losses. This paves the way for cost-optimized choices of handling equipment in industrial N.I.C.E.-wire production lines.
Micronization of biochar (BC) may ease its application in agriculture. For example, fine biochar powders can be applied as suspensions via drip-irrigation systems or can be used to produce grnulated fertilizers. However, micronization may effect important physical biochar properties like the water holding capacity (WHC) or the porosity.
The majority of anterior cruciate ligament (ACL) injuries in team sports are non-contact injuries, with cutting maneuvers identified as high-risk tasks. Young female handball players have been shown to be at greater risk for ACL injuries than males. One risk factor for ACL injuries is the magnitude of the knee abduction moment (KAM). Cutting technique variables on foot placement, overall approach and knee kinematics have been shown to influence the KAM. Since injury risk is believed to increase with increasing task complexity, the purpose of the study was to test the effect of task complexity on technique variables that influence the KAM in female handball players during fake-and-cut tasks.
With recent developments in the Ukrainian-Russian conflict, many are discussing about Germany’s dependency on fossil fuel imports in its energy system, and how can the country proceed with reducing that dependency. With its wide-ranging consumption sectors, the electricity sector comes as the perfect choice to start with. Recent reports showed that the German federal government is already intending to have a fully renewable electricity by 2035 while exploiting all possible clean power options. This was published in the federal government’s climate emergency program (Easter Package) in early 2022. The aim of this package is to initiate a rapid transition and decarbonization of the electricity sector. The Easter Package expects an enormous growth of renewable energies to a completely new level, with already at least 80% renewable gross energy consumption, with extensive and broad deployment of different generation technologies on various scales. This paper will discuss this ambitious plan and outline some insights into this huge and rapidly increasing step, and show how much will Germany need in order to achieve this huge milestone towards a fully green supply of the electricity sector. Different scenarios and shares of renewables will be investigated in order to elaborate on preponed climate-neutral goal of the electricity sector by 2035. The results pointed out some promising aspects in achieving a 100% renewable power, with massive investments in both generation and storage technologies.
The sharp rise in electricity and oil prices due to the war in Ukraine has caused fluctuations in the results of the previous study about the economic analysis of electric buses. This paper shows how the increase in fuel prices affects the implementation of electric buses. This publication is constructing the Total Cost of Ownership (TCO) model in the small-mid-size city, Offenburg for the transition to electric buses. The future development of costs is estimated and a projection based on learning curves will be carried out. This study intends to introduce a new future prospect by presenting the latest data based on previous research. Through the new TCO result, the cost differences between the existing diesel bus and the electric bus are updated, and also the future prospects for the economic feasibility of the electric bus in a small and midsize city are presented.
Due to the Covid-19 pandemic, the RoboCup WorldCup 2021 was held completely remotely. For this competition the Webots simulator (https://cyberbotics.com/) was used, so all teams needed to transfer their robot to the simulation. This paper describes our experiences during this process as well as a genetic learning approach to improve our walk engine to allow a more stable and faster movement in the simulation. Therefore we used a docker setup to scale easily. The resulting movement was one of the outstanding features that finally led to the championship title.
An import ban of Russian energy sources to Germany is currently being increasingly discussed. We want to support the discussion by showing a way how the electricity system in Germany can manage low energy imports in the short term and which measures are necessary to still meet the climate protection targets. In this paper, we examine the impact of a complete stop of Russian fossil fuel imports on the electricity sector in Germany, and how this will affect the climate coals of an earlier coal phase-out and climate neutrality by 2045.
Following a scenario-based analysis, the results gave a point of view on how much would be needed to completely rely on the scarce non-renewable energy resources in Germany. Huge amounts of investments would be needed in order to ensure a secure supply of electricity, in both generation energy sources (RES) and energy storage systems (ESS). The key findings are that a rapid expansion of renewables and storage technologies will significantly reduce the dependence of the German electricity system on energy imports. The huge integration of renewable energy does not entail any significant imports of the energy sources natural gas, hard coal, and mineral oil, even in the long term. The results showed that a ban on fossil fuel imports from Russia outlines huge opportunities to go beyond the German government's climate targets, where the 1.5-degree-target is achieved in the electricity system.
The energy system is changing since some years in order to achieve the climate goals from the Paris Agreement which wants to prevent an increase of the global temperature above 2 °C [1]. Decarbonisation of the energy system has become for governments a big challenge and different strategies are being stablished. Germany has set greenhouse gas reduction limits for different years and keeps track of the improvement made yearly. The expansion of renewable energy systems (RES) together with decarbonisation technologies are a key factor to accomplish this objective.
This research is done to analyse the effect of introducing biochar, a decarbonisation technology, and study how it will affect the energy system. Pyrolysis is the process from which biochar is obtained and it is modelled in an open-source energy system model. A sensibility analysis is done in order to assess the effect of changing the biomass potential and the costs for pyrolysis.
The role of pyrolysis is analysed in the form of different future scenarios for the year 2045 to evaluate the impact when the CO2 emission limit is zero. All scenarios are compared to the reference scenario, where pyrolysis is not considered.
Results show that biochar can be used to compensate the emissions from other conventional power plant and achieve an energy transition with lower costs. Furthermore, it was also found that pyrolysis can also reduce the need of flexibility. This study also shows that the biomass potential and the pyrolysis costs can strongly affect the behaviour of pyrolysis in the energy system.
Eco-Feasibility Study and Application of Natural Inventive Principles in Chemical Engineering Design
(2022)
The early stages of the front-end process development are critical for the future success of projects involving new technologies. The application of eco-inventive principles identified in natural systems to the design of chemical processes and equipment allows one to find ways to mitigate or avoid secondary ecological problems such as, for example, higher consumption of raw materials or energy, generation of hazardous waste and pollution of the environment by toxic chemicals. However, before implementing a new technology in a real operational environment, it is necessary to completely investigate its undesirable ecological impact and to evaluate the future viability of this technology. Therefore, the research paper presents a study of ecological feasibility of an innovative process design utilising natural eco-inventive principles and analyses the correlations between applied inventive principles. Such eco-feasibility study can be considered as an important decision gate to determine whether the technology implementation should be moved forward. Furthermore, the study evaluates the practicability of natural inventive principles to the eco-friendly process design and is illustrated with an example of a sustainable technology for nickel extraction from pyrophyllite.
Rising societies’ demands require more sustainable products and technologies. Although numerous methods and tools have been developed in the last decades to support environmental-friendly product and process development, an interdisciplinary knowledge base of eco-innovative examples linked to the eco-innovative problems and solution principles is lacking. The paper proposes an ontology of examples for eco-friendly products and technologies assigned to the Inventive Principles (IPs) of the TRIZ methodology in accordance with the German TRIZ Standard VDI 4521. The examples of sustainable technologies and products build a database for sharing and reusing eco-innovation knowledge. The ontology acts as a tool for systematic solving of specific environmental problems in typical life cycle phases, for different environmental impact categories and engineering domains. Finally, the paper defines a future research agenda in the field of the TRIZ-based systematic eco-innovation.
The purpose of this study was to describe the effects of running speed and slope on metatarsophalangeal (MTP) joint kinematics. 22 male and female runners underwent 3D motion analysis on an instrumented treadmill at three different speeds (2.5 m/s, 3.0 m/s, 3.5 m/s). At each speed, participants ran at seven slope conditions (downhill: -15%, -10%, -5%, level, and uphill: +5%, +10%, +15%). We found a significant main effect (p < 0.001) of running speed and slope on peak MTP dorsiflexion and a running speed by slope interaction effect (p < 0.001) for peak MTP dorsiflexion velocity. These findings highlight the need to consider running intensity and environmental factors like running surface inclination when considering MTP joint mechanics and technological aids to support runners.
Effect of downhill running on biomechanical risk factors associated with iliotibial band syndrome
(2022)
The purpose of this study was to identify the influence of downhill running on biomechanical risk factors for iliotibial band syndrome. We conducted a 3D motion analysis of 22 females and males running on an instrumented treadmill at four different inclinations (0%, -5%, -10%, -15%) at a speed of 3.5 m/s. We found significant differences for biomechanical risk factors associated with iliotibial band syndrome. Peak knee flexion angle at initial ground contact (p < .001), peak knee adduction angle (p = .005), and iliotibial band strain (p < .001) systematically increased with increasing slope. Downhill running increases biomechanical risk factors for iliotibial band syndrome. Our results highlight the need to consider the individual running environment in assessing overuse injury risk in runners.
This study aimed to compare a simplified calculation of the knee abduction moment with the traditional inverse dynamics calculation when athletes perform fake-cut maneuvers with different complexities. In the simplified calculation, we multiply the force vector with its lever arm to the knee, projected onto the local coordinate system of the proximal thigh, hence neglecting the inertial contributions from distal segments. We found very strong ranking consistency using Spearman’s rank correlation coefficient when using the simplified method compared to the traditional calculation. Independent of the tasks, the simplified method resulted in higher moments than the inverse dynamics. This was caused by ignoring the moment caused by segment linear acceleration generating a counteracting moment by about 7%. An alternative to the complex calculations of inverse dynamics can be used to investigate the contributions of the GRF magnitude and its lever arm to the knee.
The purpose of this study was to 1) compare knee joint kinematics and kinetics of fake-and-cut tasks of varying complexity in 51 female handball players and 2) present a case study of one athlete who ruptured her ACL three weeks post data collection. External knee joint moments and knee joint angles in all planes at the instance of the peak external knee abduction moment (KAM) as well as moment and angle time curves were analyzed. Peak KAMs and knee internal rotation moments were substantially higher than published values obtained during simple change-of-direction tasks and, along with flexion angles, differed significantly between the tasks. Introducing a ball reception and a static defender increased joint loads while they partially decreased again when anticipation was lacking. Our results suggest to use game-specific assessments of injury risk while complexity levels do not directly increase knee loading. Extreme values of several risk factors for a post-test injured athlete highlight the need and usefulness of appropriate screenings.
To achieve Germany's climate targets, the industrial sector, among others, must be transformed. The decarbonization of industry through the electrification of heating processes is a promising option. In order to investigate this transformation in energy system models, high-resolution temporal demand profiles of the heat and electricity applications for different industries are required. This paper presents a method for generating synthetic electricity and heat load profiles for 14 industry types. Using this methodology, annual profiles with a 15-minute resolution can be generated for both energy demands. First, daily profiles for the electricity demand were generated for 4 different production days. These daily profiles are additionally subdivided into eight end-use application categories. Finally, white noise is applied to the profile of the mechanical drives. The heat profile is similar to the electrical but is subdivided into four temperature ranges and the two applications hot water and space heating. The space heating application is additionally adjusted to the average monthly outdoor temperature. Both time series were generated for the analysis of an electrification of industrial heat application in energy system modelling.
The contribution of the RoofKIT student team to the SDE 21/22 competition is the extension of an existing café in Wuppertal, Germany, to create new functions and living space for the building with simultaneous energetic upgrading. A demonstration unit is built representing a small cut-out of this extension. The developed energy concept was thoroughly simulated by the student team in seminars using Modelica. The system uses mainly solar energy via PVT collectors as the heat source for a brine-water heat pump (space heating and hot water). Energy storage (thermal and electrical) is installed to decouple generation and consumption. Simulation results confirm that carbon neutrality is achieved for the building operation, consuming and generating around 60 kWh/m2a.
Peer-to-peer energy trading and local electricity markets have been widely discussed as new options for the transformation of the energy system from the traditional centralized scheme to the novel decentralized one. Moreover, it has also been proposed as a more favourable alternative for already expiring feed in tariff policies that promote investment in renewable energy sources. Peer-to-peer energy trading is usually defined as the integration of several innovative technologies, that enable both prosumers and consumers to trade electricity, without intermediaries, at a consented price. Furthermore, the techno-economic aspects go hand in hand with the socio-economic aspects, which represent at the end significant barriers that need to be tackled to reach a higher impact on current power systems. Applying a qualitative analysis, two scalable peer-to-peer concepts are presented in this study and the possible participant´s entry probability into such concepts. Results show that consumers with a preference for environmental aspects have in general a higher willingness to participate in peer-to-peer energy trading. Moreover, battery storage systems are a key technology that could elevate the entry probability of prosumers into a peer-to-peer market.
Most recently, the federal government in Germany published new climate goals in order reach climate neutrality by 2045. This paper demonstrates a path to a cost optimal energy supply system for the German power grid until the year 2050. With special regard to regionality, the system is based on yearly myopic optimization with the required energy system transformation measures and the associated system costs. The results point out, that energy storage systems (ESS) are fundamental for renewables integration in order to have a feasible energy transition. Moreover, the investment in storage technologies increased the usage of the solar and wind technologies. Solar energy investments were highly accompanied with the installation of short-term battery storage. Longer-term storage technologies, such as H2, were accompanied with high installations of wind technologies. The results pointed out that hydrogen investments are expected to overrule short-term batteries if their cost continues to decrease sharply. Moreover, with a strong presence of ESS in the energy system, biomass energy is expected to be completely ruled out from the energy mix. With the current emission reduction strategy and without a strong presence of large scale ESS into the system, it is unlikely that the Paris agreement 2° C target by 2050 will be achieved, let alone the 1.5° C.
Lithium-ion batteries show strongly nonlinear behaviour regarding the battery current and state of charge. Therefore, the modelling of lithium-ion batteries is complex. Combining physical and data-driven models in a grey-box model can simplify the modelling. Our focus is on using neural networks, especially neural ordinary differential equations, for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis for the grey-box model. Unknown parameters and dependencies are then replaced by learnable parameters and neural networks. We use experimental full-cycle data and data from pulse tests of a lithium iron phosphate cell to train the model. Finally, we test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.
We describe a prototype for power line communi- cation for grid monitoring. The PLC receiver is used to gain information about the PLC channel and the current state of the power grid. The PLC receiver uses the communication signal to obtain an accurate estimate of the current channel and provides information which can be used as a basis for further processing with the aim to detect partial discharges and other anomalies in the grid. This monitoring of the power grid takes advantage of existing PLC infrastructure and uses the data signals, which are transmitted anyway to obtain a real-time measurement of the channel transfer function and the received noise signal. Since this signal is sampled at a high sampling rate compared to simpler measurement sensors, it contains valuable information about possible degradations in the grid which need to be addressed. While channel measurements are based on a received PLC signal, information about partial discharges or other sources of interference can be gathered by a PLC receiver in the absence of a transmit signal. A prototype based on Software Defined Radio has been developed, which implements the simultaneous communication and sensing for a power grid.
This study aims to investigate the individual response concerning BRFs for AT when the mid-sole hardness underneath the rearfoot was systematically altered. We first identified FGs based on the footwear condition that minimised the risk for AT across BRFs. We then tested the FGs for differences in anthropometrics, footwear comfort, and running characteristics.
For some years now, additive manufacturing (AM) has offered an alternative to conventional manufacturing processes. The strengths of AM are primarily the rapid implementation of ideas into a usable product and the ability to produce geometrically complex shapes. It has also significantly advanced the lightweight design of products made of plastic. So far, the strength of printed components made of polymers is previously very limited.
Recently, new AM processes have become available that allow the embedding of short and also long fibers in polymer matrix. Thus, the manufacturing of components that provide a significant increase in strength becomes possible. In this way, both complex geometries and sophisticated applications can be implemented. This paper therefore investigates how this new technology can be implemented in product development, focusing on sports equipment. An extensive literature research shows that lightweight design plays a decisive role in sports equipment. In addition, the advantages of AM in terms of individualized products and low quantities can be fully exploited.
An example of this approach is the steering system for a seat sled used by paraplegic athletes in the Olympic discipline of Nordic paraskiing. A particular challenge here is the placement and alignment of the long carbon fibers within the polymer matrix and the verification of the strength by means of Finite-Element-Analysis (FEA). In addition, findings from bionics are used to optimize the lightweight design of the steering system. Using this example, it can be shown that the weight of the steering system can be drastically reduced compared to conventional manufacturing. At the same time, a number of parts can be saved through function integration and thus the manufacturing and assembly effort can be reduced significantly.
The internal crowdsourcing-based ideation within a company can be defined as an involvement of its staff, specialists, managers, and other employees, to propose solution ideas for a pre-defined problem. This paper addresses a question, how many participants of the company-internal ideation process are required to nearly reach the ideation limit for the problems with a finite number of workable solutions. To answer the research question, the author proposes a set of metrics and a non-linear ideation performance function with a positive decreasing slope and ideation limit for the closed-ended problems. Three series of experiments helped to explore relationships between the metric attributes and resulted in a mathematical model which allows companies to predict the productivity metrics of their crowdsourcing ideation activities such as quantity of different ideas and ideation limit as a function of the number of contributors, their average personal creativity and ideation efficiency of a contributors’ group.
Offenburg university of Applied Sciences offers pre-study extracurricular preparatory courses for future engineering students in mathematics and physics. Due to pandemic restrictions, the two-week preparatory physics course preceeding winter term 2020/21 was presented as an online -only course.
Students enrolled to the course attended eight online lect ures of approximately 90 minutes duration followed by a group assignment. Both lectures and tutoring to the group assignment used a videoconference system with group sizes of 120 (lecture) and 6 (peer instruction and group assignments). The eight lectures focused on the high school physics curriculum of mechanics, electricity, thermodynamics and optics. Each lecture included four “peer instruction” questions to improve student activation. Student responses were collected using an audience response online tool.
The “peer instruction” questions were discussed by the students in online groups of six students. These groups also received written group assignments consisting of common textbook exercises and additional problems with incomplete information. To solve these problems, groups were encouraged to discuss possible solutions. The on-line course attendance was monitored and showed a characteristic exponential “decay” curve with a half-life of approximately 18 lectures which is comparable to conventional courses: Around 73% of the students enrolled in the preparatory course attended all eight lectures. In addition to the attendance, the progress of the participants was monitored by two online tests: A pre-course online test the first course day and a post -course online test on the last day.
The completion of both tests was highly recommended, but not a formal requirement for the students. The fraction of students completing the pre-course, but not the post-course test was used as an estimate for the drop-out rate of (34±3)%.
The twin concept is increasingly used for optimization tasks in the context of Industry 4.0 and digitization. The twin concept can also help small and medium-sized enterprises (SME) to exploit their energy flexibility potential and to achieve added value by appropriate energy marketing. At the same time, this use of flexibility helps to realize a climate-neutral energy supply with high shares of renewable energies. The digital twin reflects real production, power flows and market influences as a computer model, which makes it possible to simulate and optimize on-site interventions and interactions with the energy market without disturbing the real production processes. This paper describes the development of a generic model library that maps flexibility-relevant components and processes of SME, thus simplifying the creation of a digital twin. The paper also includes the development of an experimental twin consisting of SME hardware components and a PLC-based SCADA system. The experimental twin provides a laboratory environment in which the digital twin can be tested, further developed and demonstrated on a laboratory scale. Concrete implementations of such a digital twin and experimental twin are described as examples.
Grey-box modelling combines physical and data-driven models to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling, as differential equations given by physical laws and neural networks can be combined in a single modelling framework. This simplifies the simulation and optimization and allows to consider irregularly-sampled data during training and evaluation of the model. We demonstrate this approach using two levels of model complexity; first, a simple parallel resistor-capacitor circuit; and second, an equivalent circuit model of a lithium-ion battery cell, where the change of the voltage drop over the resistor-capacitor circuit including its dependence on current and State-of-Charge is implemented as NODE. After training, both models show good agreement with analytical solutions respectively with experimental data.
Do you know that for each banana bunch the complete plant must be cut as well? Only in Brazil 440 million trees are planted annually. With an average weight of 30 kg per banana plant you can estimate about 13,5 million tons of banana residues per year. Although there exist some projects to use these residues for the production of valuable products (e.g fibers for textile and paper production) most of this organic waste material is unused and left for composting on the farmland.
The basic idea of this project is to evaluate this organic waste material for converting it to a renewable and CO2 neutral fuel. Therefore, the different parts of the banana plant (heart, leaves and pseudo stem) were analyzed regarding their biogas potential (specific biogas yield and biogas production kinetics). In further studies the effect of mechanical and enzymatic pretreatments of the different parts of the plants was investigated. This examination could then be the basis for an energetic usage of this organic residue.
The biogas batch experiments were performed according to the german guideline VDI 4630 in 2-L-Batch reactors at 37°C. As biogas substrates, the heart, the leaves and the pseudo stem of the banana plant residue with and without enzymatic/mechanical pretreatment were used.
The different parts of the banana plants result in a specific biogas production yield in the range of 260-470 norm liters per kg organic dry mass.
To determine the influence of the mechanical pretreatment (particle size 1-15 mm) on the biogas production kinetics, the kinetic constants were defined and calculated. The reduction of the particle size leads to an improved biogas production kinetics. Therefore experiments will demonstrate, if the results from the batch experiments can be converted in the continuous fed biogas reactor. The experiments of the enzymatic pretreatment are still under investigation.
Environmentally-friendly implementation of new technologies and eco-innovative solutions often faces additional secondary ecological problems. On the other hand, existing biological systems show a lesser environmental impact as compared to the human-made products or technologies. The paper defines a research agenda for identification of underlying eco-inventive principles used in the natural systems created through evolution. Finally, the paper proposes a comprehensive method for capturing eco-innovation principles in biological systems in addition and complementary to the existing biomimetic methods and TRIZ methodology and illustrates it with an example.
Cross-industry innovation is commonly understood as identification of analogies and interdisciplinary transfer or copying of technologies, processes, technical solutions, working principles or models between industrial sectors. In general, creative thinking in analogies belongs to the efficient ideation techniques. However, engineering graduates and specialists frequently lack the skills to think across the industry boundaries systematically. To overcome this drawback an easy-to-use method based on five analogies has been evaluated through its applications by students and engineers in numerous experiments and industrial case studies. The proposed analogies help to identify and resolve engineering contradictions and apply approaches of the Theory of Inventive Problem Solving TRIZ and biomimetics. The paper analyses the outcomes of the systematized analogies-based ideation and outlines that its performance continuously grows with the engineering experience. It defines metrics for ideation efficiency and ideation performance function.
This book constitutes the refereed proceedings of the 20th International TRIZ Future Conference, TFC 2020, held online at the University Cluj-Napoca, Romania, in October 2020 and sponsored by the International Federation for Information Processing.
34 chapters were carefully peer reviewed and selected from 91 conference submissions. They are organized in the following thematic sections: computing TRIZ; education and pedagogy; sustainable development; tools and techniques of TRIZ for enhancing design; TRIZ and system engineering; TRIZ and complexity; and cross-fertilization of TRIZ for innovation management.
Sustainable design of equipment for process intensification requires a comprehensive and correct identification of relevant stakeholder requirements, design problems and tasks crucial for innovation success. Combining the principles of the Quality Function Deployment with the Importance-Satisfaction Analysis and Contradiction Analysis of requirements gives an opportunity to define a proper process innovation strategy more reliably and to develop an optimal process intensification technology with less secondary engineering and ecological problems.
Additive manufacturing is a rapidly growing manufacturing process for which many new processes and materials are currently being developed. The biggest advantage is that almost any shape can be produced, while conventional manufacturing methods reach their limits. Furthermore, a lot of material is saved because the part is created in layers and only as much material is used as necessary. In contrast, in the case of machining processes, it is not uncommon for more than half of the material to be removed and disposed of. Recently, new additive manufacturing processes have been on the market that enables the manufacturing of components using the FDM process with fiber reinforcement. This opens up new possibilities for optimizing components in terms of their strength and at the same time increasing sustainability by reducing materials consumption and waste. Within the scope of this work, different types of test specimens are to be designed, manufactured and examined. The test specimens are tensile specimens, which are used both for standardized tensile tests and for examining a practical component from automotive engineering used in student project. This project is a vehicle designed to compete in the Shell Eco-marathon, one of the world’s largest energy efficiency competitions. The aim is to design a vehicle that covers a certain distance with as little fuel as possible. Accordingly, it is desirable to manufacture the components with the lowest possible weight, while still ensuring the required rigidity. To achieve this, the use of fiber-reinforced 3D-printed parts is particularly suitable due to the high rigidity. In particular, the joining technology for connecting conventionally and additively manufactured components is developed. As a result, the economic efficiency was assessed, and guidelines for the design of components and joining elements were created. In addition, it could be shown that the additive manufacturing of the component could be implemented faster and more sustainably than the previous conventional manufacturing.
Short-term load forecasting (STLF) has been playing a key role in the electricity sector for several decades, due to the need for aligning energy generation with the demand and the financial risk connected with forecasting errors. Following the top-down approach, forecasts are calculated for aggregated load profiles, meaning the sum of singular loads from consumers belonging to a balancing group. Due to the emerging flexible loads, there is an increasing relevance for STLF of individual factories. These load profiles are typically more stochastic compared to aggregated ones, which imposes new requirements to forecasting methods and tools with a bottom-up approach. The increasing digitalization in industry with enhanced data availability as well as smart metering are enablers for improved load forecasts. There is a need for STLF tools processing live data with a high temporal resolution in the minute range. Furthermore, behin-the-meter (BTM) data from various sources like submetering and production planning data should be integrated in the models. In this case, STLF is becoming a big data problem so that machine learning (ML) methods are required. The research project “GaIN” investigates the improvement of the STLF quality of an energy utility using BTM data and innovative ML models. This paper describes the project scope, proposes a detailed definition for a benchmark and evaluates the readiness of existing STLF methods to fulfil the described requirements as a reviewing paper.
The review highlights that recent STLF investigations focus on ML methods. Especially hybrid models gain more and more importance. ML can outperform classical methods in terms of automation degree and forecasting accuracy. Nevertheless, the potential for improving forecasting accuracy by the use of ML models depends on the underlying data and the types of input variables. The described methods in the analyzed publications only partially fulfil the tool requirements for STLF on company level. There is still a need to develop suitable ML methods to integrate the expanded data base in order to improve load forecasts on company level.
Interaction and capturing information from the surrounding is dominated by vision and hearing. Haptics on the other side, widens the bandwidth and could also replace senses (sense switching) for impaired. Haptic technologies are often limited to point-wise actuation. Here, we show that actuation in two-dimensional matrices instead creates a richer input. We describe the construction of a full-body garment for haptic communication with a distributed actuating network. The garment is divided into attachable-detachable panels or add-ons that each can carry a two dimensional matrix of actuating haptic elements. Each panel adds to an enhanced sensoric capability of the human- garment system so that together a 720° system is formed. The spatial separation of the panels on different body locations supports semantic and theme-wise separation of conversations conveyed by haptics. It also achieves directional faithfulness, which is maintaining any directional information about a distal stimulus in the haptic input.