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Objective: This paper deals with the design and the optimization of mechatronic devices.
Introduction: Comparing with existing works, the design approach presented in this paper aims to integrate optimization in the design phase of complex mechatronic systems in order to increase the efficiency of this method.
Methods: To solve this problem, a novel mechatronic system design approach has been developed in order to take the multidisciplinary aspect and to consider optimization as a tool that can be used within the embodiment design process to build mechatronic solutions from a set of solution concepts designed with innovative or routine design methods.
Conclusions: This approach has then been applied to the design and optimization of a wind turbine system that can be implemented to autonomously supply a mountain cottage.
Automatic Identification of Travel Locations in Rare Books - Object Oriented Information Management
(2017)
The digital content of the Internet is growing exponentially and mass digitization of printed media opens access to literature, in particular the genre of travel literature from the 18th and 19th century, which consists of diaries or travel books describing routes, observations or inspirations. The identification of described locations in the digital text is a long-standing challenge which requires information technology to supply dynamic links to sources by new forms of interaction and synthesis between humanistic texts and scientific observations.
Using object oriented information technology, a prototype of a software tool is developed which makes it possible to automatically identify geographic locations and travel routes mentioned in rare books. The information objects contain properties such as names and classification codes for populated places, streams, mountains and regions. Together, with the latitudes and longitudes of every single location, it is possible to geo-reference this information in order that all processed and filtered datasets can be displayed by a map application. This method has already been used in the Humboldt Digital Library to present Alexander von Humboldt’s maps and was tested in a case study to prove the correctness and reliability of the automatic identification of locations based on the work of Alexander von Humboldt and Johann Wolfgang von Goethe.
The results reveal numerous errors due to misspellings, change of location names, equality of terms and location names. But on the other hand it becomes very clear that results of the automatic object detection and recognition can be improved by error-free and comprehensive sources. As a result an increase in quality and usability of the service can be expected, accompanied by more options to detect unknown locations in the descriptions of rare books.
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.
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.
The Advanced Innovation Design Approach is a holistic methodology for enhancing innovative and competitive capability of industrial companies. AIDA can be considered as an open mindset, an individually adaptable range of strongest innovation techniques such as comprehensive front-end innovation process, advanced innovation methods, best tools and methods of the TRIZ methodology, organizational measures for accelerating innovation, IT-solutions for Computer-Aided Innovation, and other innovation methods, elaborated in the recent decade in the industry and academia
The European TRIZ Association ETRIA acts as a connecting link between scientific institutions, universities and other educational organizations, industrial companies and individuals concerned with conceptual and practical questions relating to organization of innovation process, invention methods, and innovation knowledge. In the meantime, more than TFC 1000 papers or presentation of scientists, educators, and practitioners from all over the world are available at the official ETRIA website. Numerous research projects were supported or funded by the European Commission.
Process engineering focuses on the design, operation, control and optimization of chemical, physical and biological processes and has applications in many industries. Process Intensification is the key development approach in the modern process engineering. The proposed Advanced Innovation Design Approach (AIDA) combines the holistic innovation process with the systematic analytical and problem solving tools of the theory of inventive problem solving TRIZ. The present paper conceptualizes the AIDA application in the field of process engineering and especially in combination with the Process Intensification. It defines the AIDA innovation algorithm for process engineering and describes process mapping, problem ranking, and concept design techniques. The approach has been validated in several industrial case studies. The presented research work is a part of the European project “Intensified by Design® platform for the intensification of processes involving solids handling”.
The collection of selected papers of the TRIZ Future Conference 2017 is in open access and is included to the Innovator, the journal of the European TRIZ Assocation.
In the course of the last few years, our students are becoming increasingly unhappy. Sometimes they stop attending lectures and even seem not to know how to behave correctly. It feels like they are getting on strike. Consequently, drop-out rates are sky-rocketing. The lecturers/professors are not happy either, adopting an “I-don’t-care” attitude.
An interdisciplinary, international team set in to find out: (1) What are the students unhappy about? Why is it becoming so difficult for them to cope? (2) What does the “I-don’t-care” attitude of professors actually mean? What do they care or not care about? (3) How far do the views of the parties correlate? Could some kind of mutual understanding be achieved?
The findings indicate that, at least at our universities, there is rather a long way to go from “Engineering versus Pedagogy” to “Engineering Pedagogy”.
We present a two-dimensional (2D) planar chromatographic separation method for phytoestrogenic active compounds on RP-18 W (Merck, 1.14296) phase. It could be shown that an ethanolic extract of liquorice (Glycyrrhiza glabra) roots contains four phytoestrogenic active compounds. As solvent, in the first direction, the mix of hexane, ethyl acetate, and acetone (45:15:10, v/v) was used, and, in the second direction, that of acetone and water (15:10, v/v) was used. After separation, a modified yeast estrogen screen (YES) test was applied, using the yeast strain Saccharomyces cerevisiae BJ3505. The test strain (according to McDonnell) contains the estrogen receptor. Its activation by estrogen active compounds is measured by inducing the reporter gene lacZ which encodes the enzyme β-galactosidase. This enzyme activity is determined on plate by using the fluorescent substrate MUG (4-methylumbelliferyl-β-d-galactopyranoside). The enzyme can also hydrolyse X-β-Gal (5-bromo-4-chloro-3-indoxyl-β-d-galactopyranosid) into β-galactose and 5-bromo-4-chloro-3-indoxyl. The indoxyl compound is oxidized by oxygen forming the deep-blue dye 5,5β-dibromo-4,4β-dichloro-indigo which allows to detect phytoestrogenic activity more specific in the presence of native fluorescing compounds.
Three real-lab trigeneration microgrids are investigated in non-residential environments (educational, office/administrational, companies/production) with a special focus on domain-specific load characteristics. For accurate load forecasting on such a local level, à priori information on scheduled events have been combined with statistical insight from historical load data (capturing information on not explicitly-known consumer behavior). The load forecasts are then used as data input for (predictive) energy management systems that are implemented in the trigeneration microgrids. In real-world applications, these energy management systems must especially be able to carry out a number of safety and maintenance operations on components such as the battery (e.g. gassing) or CHP unit (e.g. regular test runs). Therefore, energy management systems should combine heuristics with advanced predictive optimization methods. Reducing the effort in IT infrastructure the main and safety relevant management process steps are done on site using a Smart & Local Energy Controller (SLEC) assisted by locally measured signals or operator given information as default and external inputs for any advanced optimization. Heuristic aspects for local fine adjustment of energy flows are presented.
Polygeneration systems are a key technology for the reduction of primary energy usage and emissions. High costs, lack of flexibility and effort for parameterization hinder the wide usage of modeling tools during their conceptual design. This paper describes how planning tools can be structured for the conceptual design phase where only little information is available to the planner. A library concept was developed using the principles of object-oriented modeling to address the flexibility issue. With respect to cost and expandability, the open-source modeling language Modelica was chosen. Furthermore, easy-to-parameterize component models were developed. In addition to the improved library concept and novel component models, an easy-to-adapt control concept is proposed. The component models were validated and the applicability of the library was demonstrated by means of an example. It was shown that the data usually obtained from spec sheets are sufficient to parameterize the models. In addition to this, the control concept was approved.
Micro gas turbines (MGTs) are regarded as combined heat and power (CHP) units which offer high fuel utilization and low emissions. They are applied in decentralized energy neration.
To facilitate the planning process of energy systems, namely in the context of the increasing application of optimization techniques, there is a need for easy-to-parametrize component models with sufficient accuracy which allow a fast computation. In this paper, a model is proposed where the non-linear part load characteristics of the MGT are linearized by means of physical insight of the working principles of turbomachinery. Further, it is shown that the model can be parametrized by the data usually available in spec sheets. With this model a uniform description of MGTs from several manufacturers
covering an electrical power range from 30kW to 333kW can be obtained. The MGT model was
implemented by means of Modelica/Dymola. The resulting MGT system model, comprising further heat exchangers and hydraulic components, was validated using the experimental data of a 65kW MGT from a trigeneration energy system.
Radiation is an important means of heat transfer inside an electric arc furnace (EAF).
To gain insight into the complex processes of heat transfer inside the EAF vessel, not only radiation from the surfaces but also emission and absorption of the gas phase and the dust cloud need to be considered.
Furthermore, the radiative heat exchange depends on the geometrical configuration which is continuously changing throughout the process.
The present paper introduces a system model of the EAF which takes into account the radiative heat transfer between the surfaces
and the participating medium. This is attained by the development of a simplified geometrical model,
the use of a weighted-sum-of-gray-gases model, and a simplified consideration of dust radiation.
The simulation results were compared with the data of real EAF plants available in literature.
The following contribution deals with the growth of cracks in low-cycle fatigue (LCF) and thermomechanical fatigue (TMF) tested specimens of Inconel 718 measured by using the replica method. The specimens are loaded with different strain rates. The material shows a significantly higher crack growth rate if the strain rate is decreased. Electron backscatter diffraction (EBSD) is adopted to identify the failure mechanism and the misorientation relationship of failed grain boundaries in secondary cracks. The analyzed cracks propagated mainly transgranular but also intergranular failure can be observed in some areas. It is found that grain boundaries with coincidence site lattice (CSL) boundary structure are generally less susceptible for intergranular failure than grain boundaries with random misorientation. For modeling the experimentally identified crack behavior an existing model for fatigue crack growth based on the mechanism of time dependent elastic–plastic crack tip blunting is enhanced to describe environmental effects based on the mechanism of oxygen diffusion at the crack tip. For the diffusion process the temperature dependent parabolic diffusion law is assumed. As a result, the time dependent cyclic crack tip opening displacement (DCTOD) is used as representative value to describe both mechanisms. Thus, most
of the included model parameters characterize the deformation behavior of the material and can be determined by independent material tests. With the determined material properties, the proposed model describes the experimentally measured crack growth curves very well. The model is validated based on predictions of the number of cycles to failure of LCF as well as in-phase and out-of-phase TMF tests in the temperature range between room temperature and 650 °C.
Cast iron materials are used as materials for cylinder heads for heavy duty internal combustion engines. These components must withstand severe cyclic mechanical and thermal loads throughout their service life. While high-cycle fatigue (HCF) is dominant for the material in the water jacket region, the combination of thermal transients with mechanical load cycles results in thermomechanical fatigue (TMF) of the material in the fire deck region, even including superimposed TMF and HCF loads. Increasing the efficiency of the engines directly leads to increasing combustion pressure and temperature and, thus, lower safety margins for the currently used cast iron materials or alternatively the need for superior cast iron materials. In this paper (Part I), the TMF properties of the lamellar graphite cast iron GJL250 and the vermicular graphite cast iron GJV450 are characterized in uniaxial tests and a mechanism-based model for TMF life prediction is developed for both materials. The model can be used to estimate the fatigue life of components by means of finite-element calculations (Part II of the paper) and supports engineers in finding the appropriate material and design. Furthermore, the effect of the elastic, plastic and creep properties of the materials on the fatigue life can be evaluated with the model. However, for a material selection also the thermophysical properties, controlling to a high level the thermal stresses in the component, must be considered. Hence, the need for integral concepts for material characterization and selection from a multitude of existing and soon-to-be developed cast iron materials is discussed.
A complete thermomechanical fatigue (TMF) life prediction methodology is developed for predicting the TMF life of cast iron cylinder heads for efficient heavy duty internal combustion engines. The methodology uses transient temperature fields as thermal loads for the non-linear structural finite-element analysis (FEA). To obtain reliable stress and strain histories in the FEA for cast iron materials, a time and temperature dependent plasticity model which accounts for viscous effects, non-linear kinematic hardening and tensioncompression asymmetry is required. For this purpose a unified elasto-viscoplastic Chaboche model coupled with damage is developed and implemented as a user material model (USERMAT) in the general purpose FEA program ANSYS. In addition, the mechanismbased DTMF model for TMF life prediction developed in Part I of the paper is extended to three-dimensional stress states under transient non-proportional loading conditions. The material properties of the plasticity model are determined for lamellar graphite cast iron GJL250 and vermicular graphite cast iron GJV450 from isothermal and non-isothermal uniaxial tests. The methodology is applied to obtain a TMF life prediction on two cast iron cylinder heads for heavy duty diesel engine applications made from both cast iron materials. It is shown that the life predictions using the developed methodology correlate very well with observed lives from two bench tests in terms of location as well as number of cycles to failure.
For the RoboCup Soccer AdultSize League the humanoid robot Sweaty uses a single fully convolutional neural network to detect and localize the ball, opponents and other features on the field of play. This neural network can be trained from scratch in a few hours and is able to perform in real-time within the constraints of computational resources available on the robot. The time it takes to precess an image is approximately 11 ms. Balls and goal posts are recalled in 99 % of all cases (94.5 % for all objects) accompanied by a false detection rate of 1.2 % (5.2 % for all). The object detection and localization helped Sweaty to become finalist for the RoboCup 2017 in Nagoya.
One of the challenges in humanoid robotics is motion control. Interacting with humans requires impedance control algorithms, as well as tackling the problem of the closed kinematic chains which occur when both feet touch the ground. However, pure impedance control for totally autonomous robots is difficult to realize, as this algorithm needs very precise sensors for force and speed of the actuated parts, as well as very high sampling rates for the controller input signals. Both requirements lead to a complex and heavy weight design, which makes up for heavy machines unusable in RoboCup Soccer competitions.
A lightweight motor controller was developed that can be used for admittance and impedance control as well as for model predictive control algorithms to further improve the gait of the robot.
Simulation-based degradation assessment of lithium-ion batteries in a hybrid electric vehicle
(2017)
The insufficient lifetime of lithium-ion batteries is one of the major cost driver for mobile applications. The battery pack in vehicles is one of the most expensive single components that practically must be excluded from premature replacement (i.e., before the life span of the other components end). Battery degradation is a complex physicochemical process that strongly depends on operating condition and environment. We present a simulation-based analysis of lithium-ion battery degradation during operation with a standard PHEV test cycle. We use detailed multiphysics (extended Newman-type) cell models that allow the assessment of local electrochemical potential, species and temperature distributions as driving forces for degradation, including solid electrolyte interphase (SEI) formation [1]. Fig. 1 shows an exemplary test cycle and the predicted resulting spatially-averaged SEI formation rate. We apply a time-upscaling approach to extrapolate the degradation analysis over long time scales, keeping physical accuracy while allowing end-of-life assessment [2]. Results are presented for lithium-ion battery cells with graphite/LFP chemistry. The behavior of these cells in terms of degradation propensity, performance, state of charge and other internal states is predicted during long-term cycling. State of health (SOH) is quantified as capacity fade and internal resistance increase as function of operation time.
Practical bottlenecks associated with commercialization of Lithium-air cells include capacity limitation and low cycling efficiency. The origin of such losses can be traced to complex electrochemical side reactions and reactant mass transport losses[1]. The efforts to minimize such losses include exploration of various electrolytes with additives[2], and cell component geometry and material design. Given the wide range of options for such materials, it is almost impractical to experimentally setup and characterize all those cells. Consequently, modeling and simulation studies are efficient alternatives to analyze spatially and temporally resolved cell behavior for various combinations of materials[3]. In this study, with the help of a two-dimensional multi physics model, we have focused on the effect of electrode and electrolyte interaction (electrochemistry), choice of electrolyte (species transport), and electrode geometry (electrode design) on the performance of a lithium-air button cell. Figure1a shows the schematics of the 2D axisymmetric computational domain. A comparative analysis of five different electrolytes was performed while focusing on the 2D distribution of local current density and the concentration of electro-chemically active species in the cell, that is, O2and Li+. Using two different cathode configurations, namely, flooded electrode and gas diffusion electrode (GDE)[4] at different cathode thickness, the effect of cell geometry and electrolyte saturation on cell performance was explored. Further, a detailed discussion on electrode volume utilization (cf. Figure1b) is presented via changes in the active volume of cathode that produces 90% of the total current with the cell current density for different combinations of electrolyte saturations and cathode thickness.
Simulation-based degradation assessment of lithium-ion batteries in a hybrid electric vehicle
(2017)
Muli-scale thermos-electrochemical modelling of aging mechanisms in an LFP/graphite lithium-ion cell
(2017)
Passive hybridization of battery cell and photovoltaic cell: modeling and experimental validation
(2017)
Electrochemical impedance spectroscopy (EIS) is a widely-used diagnostic technique to characterize electrochemical processes. It is based on the dynamic analysis of two electrical observables, that is, current and voltage. Electrochemical cells with gaseous reactants or products (e.g., fuel cells, metal/air cells, electrolyzers) offer an additional observable, that is, the gas pressure. The dynamic coupling of current and/or voltage with gas pressure gives rise to a number of additional impedance definitions, for which we have introduced the term electrochemical pressure impedance spectroscopy (EPIS) [1,2]. EPIS shows a particular sensitivity towards transport processes of gas-phase or dissolved species, in particular, diffusion coefficients and transport pathway lengths. It is as such complementary to standard EIS, which is mainly sensitive towards electrochemical processes. This sensitivity can be exploited for model parameterization and validation. A general analysis of EPIS is presented, which shows the necessity of model-based interpretation of the complex EPIS shapes in the Nyquist plot (cf. Figure). We then present EPIS simulations for two different electrochemical cells: (1) a sodium/oxygen battery cell and (2) a hydrogen/air fuel cell. We use 1D or 2D electrochemical and transport models to simulate current excitation/pressure detection or pressure excitation/voltage detection. The results are compared to first EPIS experimental data available in literature [2,3].
Battery degradation is a complex physicochemical process that strongly depends on operating conditions. We present a model-based analysis of lithium-ion battery degradation in a stationary photovoltaic battery system. We use a multi-scale multi-physics model of a graphite/lithium iron phosphate (LiFePO4, LFP) cell including solid electrolyte interphase (SEI) formation. The cell-level model is dynamically coupled to a system-level model consisting of photovoltaics (PV), inverter, load, grid interaction, and energy management system, fed with historic weather data. Simulations are carried out for two load scenarios, a single-family house and an office tract, over annual operation cycles with one-minute time resolution. As key result, we show that the charging process causes a peak in degradation rate due to electrochemical charge overpotentials. The main drivers for cell ageing are therefore not only a high state of charge (SOC), but the charging process leading towards high SOC. We also show that the load situation not only influences system parameters like self-sufficiency and self-consumption, but also has a significant impact on battery ageing. We assess reduced charge cut-off voltage as ageing mitigation strategy.
The DMFC is a promising option for backup power systems and for the power supply of portable devices. However, from the modeling point of view liquid-feed DMFC are challenging systems due to the complex electrochemistry, the inherent two-phase transport and the effect of methanol crossover. In this paper we present a physical 1D cell model to describe the relevant processes for DMFC performance ranging from electrochemistry on the surface of the catalyst up to transport on the cell level. A two-phase flow model is implemented describing the transport in gas diffusion layer and catalyst layer at the anode side. Electrochemistry is described by elementary steps for the reactions occurring at anode and cathode, including adsorbed intermediate species on the platinum and ruthenium surfaces. Furthermore, a detailed membrane model including methanol crossover is employed. The model is validated using polarization curves, methanol crossover measurements and impedance spectra. It permits to analyze both steady-state and transient behavior with a high level of predictive capabilities. Steady-state simulations are used to investigate the open circuit voltage as well as the overpotentials of anode, cathode and electrolyte. Finally, the transient behavior after current interruption is studied in detail.
Within this work, the benefits of using predictive control methods for the operation of Adsorption Cooling Machines (ACMs) are shown on a simulation study. Since the internal control decisions of series-manufactured ACMs often cannot be influenced, the work focuses on optimized scheduling of an ACM considering its internal functioning as well as forecasts for load and driving energy occurrence. For illustration, an assumed solar thermal climate system is introduced and a system model suitable for use within gradient-based optimization methods is developed. The results of a system simulation using a conventional scheme for ACM scheduling are compared to the results of a predictive, optimization-based scheduling approach for the same exemplary scenario of load and driving energy occurrence. The benefits of the latter approach are shown and future actions for application of these methods for system control are addressed.
We present a two-dimensional (2D) planar chromatographic separation of estrogenic active compounds on RP-18 W (Merck, 1.14296) phase. A mixture of 8 substances was separated using a solvent mix consisting of hexane, ethyl acetate, acetone (55:15:10, v/v) in the first direction and of acetone and water (15:10, v/v) in the second direction. Separation was performed on an RP-18 W plate over a distance of 70 mm. This 2D-separation method can be used to quantify 17α-ethinylestradiol (EE2) in an effect-directed analysis, using the yeast strain Saccharomyces cerevisiae BJ3505. The test strain (according to McDonnell) contains the estrogen receptor. Its activation by estrogen active compounds is measured by inducing the reporter gene lacZ which encodes the enzyme β-galactosidase. This enzyme activity is determined on plate by using the fluorescent substrate MUG (4-methylumbelliferyl-β-d-galactopyranoside).
Modelling and Simulation of Microscale Trigeneration Systems Based on Real- Life Experimental Data
(2017)
For the shift of the energy grid towards a smarter decentralised system flexible microscale trigeneration systems will play an important role due to their ability to support the demand side management in buildings. However to harness their potential modern control methods like model predictive control must be implemented for their optimal scheduling and control. To implement such supervisory control methods, first, simple analytical models representing the behaviour of the components need to be developed. At the Institute of Energy System Technologies in Offenburg we have built a real-life microscale trigeneration plant and present in this paper the models based on experimental data. These models are qualitatively validated and their application in the future for the optimal scheduling problem is briefly motivated.
Microscale trigeneration systems are highly flexible in their operation and thus offer the technical possibility for peak load shifting in building demand side management. However to harness their potential modern control methods such as model predictive control must be implemented for their optimal scheduling. In literature the need for experimental investigation of microscale trigeneration systems to identify typical characteristics of the components and their interactions has been identified. On a real-life setup control specific information of the components is collected and lessons learnt during commissioning of the equipment is shared. The data is analysed to draw the vital characteristics of the system and it will be used for creating models of the components that can be utilised for optimal control.
Biological in situ methanation: Gassing concept and feeding strategy for enhanced performance
(2017)
The expansion of fluctuating renewable electricity production from wind and solar energy requires huge storage capacities. Power-to-gas (PtG) can contribute to tackle that issue via a two-step process, the electrolytic production of hydrogen and a subsequent methanation step (with additional CO2). The resulting fully grid compatible methane, also known as synthetic natural gas (SNG), can be both stored and transported in the vast existing natural gas infrastructure.
To overcome current major drawbacks of PtG, the relatively low efficiency and the high costs, we developed an improved method for the methanation step. In our approach we use a further development of the biological in situ methanation of hydrogen in biogas plants. Because this strategy uses directly internal residual CO2 from the biogas process in the biogas plant, you neither need additional external CO2 nor special reactors. Thus, PtG is combined with the production of an upgraded highly methane rich raw biogas.
However, the low solubility of hydrogen in aqueous solutions and the exploitation of the maximum biological production rates are still an engineering challenge for high performance biological in situ methanation.
In our experiments a setup with membrane gassing turned out to be most promising to ensure a sufficient gas liquid mass transfer of the hydrogen. The monitoring of hydrogenotrophic and aceticlastic archaea showed some adaption of these microbial subgroups to the hydrogen feed.
In order to achieve high methane concentrations of more than 90 % in the raw biogas a CO2-controlled hydrogen feed flow rate is suggested. For methane concentrations lower than 90 % simple current controlled hydrogen supply can be applied.