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Broad acceptance of finite-element-based analysis of structural problems and the increased availability of CAD-systems for structural tasks, which help to generate meshes of non-trivial geometries, have been setting a standard for the evaluation of designs in mechanical engineering in the last few decades. The development of automated or semi-automated optimizers, integrated into the Computer-Aided Engineering (CAE)-packages or working as outer loop machines, requiring the solver to do the analysis of the specific designs, has been accepted by most advanced users of the simulation community as well. The availability and inexpensive processing power of computers is increasing without any limitations foreseen in the coming years. There is little doubt that virtual product development will continue using the tools that have proved to be so successful and so easy to handle.
Current fields of interest
(2016)
If we review the research done in the field of optimization, the following topics appear to be the focus of current development:
– Optimization under uncertainties, taking into account the inevitable scatter of parts, external effects and internal properties. Reliability and robustness both have to be taken into account when running optimizations, so the name Robust Design Optimization (RDO) came into use.
– Multi-Objective Optimization (MOO) handles situations in which different participants in the development process are developing in different directions. Typically we think of commercial and engineering aspects, but other constellations have to be looked at as well, such as comfort and performance or price and consumption.
– Process development of the entire design process, including optimization from early stages, might help avoid inefficient efforts. Here the management of virtual development has to be re-designed to fit into a coherent scheme.
...
There are many other fields where interesting progress is being made. We limit our discussion to the first three questions.
To illustrate the power and the pitfalls of Bionic Optimization, we will show some examples spanning classes of applications and employing various strategies. These applications cover a broad range of engineering tasks. Nevertheless, there is no guarantee that our experiences and our examples will be sufficient to deal with all questions and issues in a comprehensive way. As general rule it might be stated, that for each class of problems, novices should begin with a learning phase. So, in this introductory phase, we use simple and quick examples, e.g., using small FE-models, linear load cases, short time intervals and simple material models. Here beginners within the Bionic Optimization community can learn which parameter combinations to use. In Sect. 3.3 we discuss strategies for optimization study acceleration. Making use of these parameters as starting points is one way to set the specific ranges, e.g., number of parents and kids, crossing, mutation radii and, numbers of generations. On the other hand, these trial runs will doubtless indicate that Bionic Optimization needs large numbers of individual designs, and considerable time and computing power. We recommend investing enough time preparing each task in order to avoid the frustration should large jobs fail after long calculation times.
Application to CAE systems
(2016)
Due to the broad acceptance of CAD-systems based on 3D solids, the geometric data of all common CAE (Computer-Aided Engineering) software, at least in mechanical engineering, are based on these solids. We use solid models, where the space filled by material is defined in a simple and easily useable way. Solid models allow for the development of automated meshers that transform solid volumes into finite elements. Even after some unacceptable initial trials, users are able to generate meshes of non-trivial geometries within minutes to hours, instead of days or weeks. Once meshing had no longer been the cost limiting factor of finite element studies, numerical simulation became a tool for smaller industries as well.
Due to the broad acceptance of CAD-systems based on 3D solids , the geometric data of all common CAE (Computer-Aided Engineering) software, at least in mechanical engineering, are based on these solids. We use solid models , where the space filled by material is defined in a simple and easily useable way. Solid models allow for the development of automated meshers that transform solid volumes into finite elements. Even after some unacceptable initial trials, users are able to generate meshes of non-trivial geometries within minutes to hours, instead of days or weeks. Once meshing had no longer been the cost limiting factor of finite element studies, numerical simulation became a tool for smaller industries as well.
In the early days of automated meshing development, there were discussions over the use of tetragonal (Fig. 4.1) or hexagonal based meshes. But, after a short period of time, it became evident, that there were and will always be many problems using automated meshers to generate hexagonal elements . So today nearly all automated 3D-meshing systems use tetragonal elements .
We have seen that bionic optimization can be a powerful tool when applied to problems with non-trivial landscapes of goals and restrictions. This, in turn, led us to a discussion of useful methodologies for applying this optimization to real problems. On the other hand, it must be stated that each optimization is a time consuming process as soon as the problem expands beyond a small number of free parameters related to simple parabolic responses. Bionic optimization is not a quick approach to solving complex questions within short times. In some cases it has the potential to fail entirely, either by sticking to local maxima or by random exploration of the parameter space without finding any promising solutions. The following sections present some remarks on the efficiency and limitations users must be aware of. They aim to increase the knowledge base of using and encountering bionic optimization. But they should not discourage potential users from this promising field of powerful strategies to find good or even the best possible designs.
In this chapter we introduce methods to improve mechanical designs by bionic methods. In most cases we assume that a general idea of the part or system is given by a set of data or parameters. Our task is to modify these free parameters so that a given goal or objective is optimized without violation of any of the existing restrictions.
Motivation
(2016)
Since human beings started to work consciously with their environment, they have tried to improve the world they were living in. Early use of tools, increasing quality of these tools, use of new materials, fabrication of clay pots, and heat treatment of metals: all these were early steps of optimization. But even on lower levels of life than human beings or human society, we find optimization processes. The organization of a herd of buffalos to face their enemies, the coordinated strategies of these enemies to isolate some of the herd’s members, and the organization of bird swarms on their long flights to their winter quarters: all these social interactions are optimized strategies of long learning processes, most of them the result of a kind of collective intelligence acquired during long selection periods.
Today the optimization of metal forming processes is done using advanced simulation tools in a virtual process, e.g. FEM-studies. The modification of the free parameters represents the different variants to be analysed. So experienced engineers may derive useful proposals in an acceptable time if good initial proposals are available. As soon as the number of free parameters growths or the total process takes long times and uses different succeeding forming steps it might be quite difficult to find promising initial ideas. In metal forming another problem has to be considered. The optimization using a series of local improvements, often called a gradient approach may find a local optimum, but this could be far away from a satisfactory solution. Therefore non-deterministic approaches, e.g. Bionic Optimization have to be used. These approaches like Evolutionary Optimization or Particle Swarm Optimization are capable to cover a large range of high dimensional optimization spaces and discover many local optima. So the chance to include the global optimum increases when using such non-deterministic methods. Unfortunately these bionic methods require large numbers of studies of different variants of the process to be optimized. The number of studies tends to increase exponentially with the number of free parameters of the forming process. As the time for one single study might be not too small as well, the total time demand will be inacceptable, taking weeks to months even if high performance computing will be used. Therefore the optimization process needs to be accelerated. Among the many ideas to reduce the time and computer power requirement Meta- and Hybrid Optimization seem to produce the most efficient results. Hybrid Optimization often consists of global searches of promising regions within the parameter space. As soon as the studies indicate that there could be a local optimum, a deterministic study tries to identify this local region. If it shows better performance than other optima found until now, it is preserved for a more detailed analysis. If it performs worse than other optima the region is excluded from further search. Meta-Optimization is often understood as the derivation of Response Surfaces of the functions of free parameters. Once there are enough studies performed, the optimization is done using the Response Surfaces as representatives e.g. for the goal and the restrictions of the optimization problem. Having found regions where interesting solutions are to be expected, the studies available up to now are used to define the Response Surfaces. In many cases low degree polynomials are used, defining their coefficients by least square methods. Both proposals Hybrid Optimization and Meta-Optimization, sometimes used in combination often help to reduce the total optimization processes by large numbers of variants to be studied. In consequence they are highly recommended when dealing with time consuming optimization studies.
In this article an energy harvesting system for measuring the wind speed starting from 2 m/s (about 2 Bft) is presented, which uses the same source for measuring and supplying power (energy autarkic). The use of the same source for measurement and power supply increases the number of potential applications since needed power is present with the measuring signal. For the case of measuring the wind velocity, one might consider applications in tunnels, tubes, pipelines, air conditioning or for controlling clogging of filters. Bluetooth Low Energy (BLE) is chosen as radio technology, since it provides the possibility to realize a unidirectional communication; requiring only a single telegram (advertising telegram) for sending the measured value. A more complex establishment of communication required by WLAN or 6LoWPAN could therefore be avoided to significantly reduce the overall energy consumption. Since the advertisement telegram does not make any provision for security or against hacking in general, a security concept is presented which includes encryption and resilience against replay attacks in a unidirectional communication system.
To facilitate the presented concepts beyond wind sensors, the system is divided into three major modules namely the generator-sensor module, the radio module and the energy management module. Whereas the first two might be changed in different applications the energy management module could be reused in many different applications. It supplies and stores the needed energy and switches power on and off in a deterministic way to ensure the radio module can operate correctly.
We present a fully automatic approach to real-time 3D face reconstruction from monocular in-the-wild videos. We use a 3D morphable face model to obtain a semi-dense shape and combine it with a fast median-based super-resolution technique to obtain a high-fidelity textured 3D face model. Our system does not need prior training and is designed to work in uncontrolled scenarios.
Mit dem Betrieb von KWK-Anlagen lässt sich nennenswert Primärenergie einsparen. KWK-Anlagen werden aus diesem Grund aufgrund verschiedener Gesetze und Richtlinien gefördert. Zum wirtschaftlichen Betrieb einer KWK-Anlage ist es erforderlich, den größtmöglichen Teil des erzeugten elektrischen Stroms entweder selbst zu verbrauchen oder an Dritte (Mieter, Wohnungseigentümer…) zu verkaufen. Mit dem KWKG 2016 werden größere KWK-Anlagen interessant, und Anlagen mit geringerer jährlicher Laufzeit können sich sogar wirtschaftlicher darstellen als reine Grundlastanlagen.
It is assumed that more education leads to better understanding of complex systems. Some researchers, however, find indications that simple mechanisms like stocks and flows are not well understood even by people who have passed higher education. In this paper, we test people’s understanding of complex systems with the widely studied stock-and-flow (SF) tasks. SF tasks assess people’s understanding of the interplay between stocks and flows. We investigate SF failure of domain experts and novices in different knowledge domains. In particular, we compare performance on the original study’s bathtub task with the square wave pattern with two alternative cover stories from the engineering and business domains on different groups of business and engineering students from different semesters. Further, we show that, while engineering students perform better than business students, with progressing in higher education, students may lose the capability of dealing with simple SF tasks. We thus find hints on déformation professionelle in higher education.
Due to the complexity of assembly processes, a high ratio of tasks is still performed by human workers. Short-cyclically changing work contents due to smaller lot sizes, especially the varied series assesmbly, increases both the need for information support as well as the risk of rising physical and psychological stress. The use of technical and digital assistance systems can counter these challenges. Through the integration of information and communication technology as well as collaborative assembly technologies, hybrid cyber-physical assembly systems will emerge. Widely established assembly planning approaches for digital and technical support systems in cyber physical assembly systems will be outlined and discussed with regard to synergies and delimitations of planning perspectives.
This paper investigates the electrothermal stability and the predominant defect mechanism of a Schottky gate AlGaN/GaN HEMT. Calibrated 3-D electrothermal simulations are performed using a simple semiempirical dc model, which is verified against high-temperature measurements up to 440°C. To determine the thermal limits of the safe operating area, measurements up to destruction are conducted at different operating points. The predominant failure mechanism is identified to be hot-spot formation and subsequent thermal runaway, induced by large drain–gate leakage currents that occur at high temperatures. The simulation results and the high temperature measurements confirm the observed failure patterns.
Purpose: Human breath analysis is proposed with increasing frequency as a useful tool in clinical application. We performed this study to find the characteristic volatile organic compounds (VOCs) in the exhaled breath of patients with idiopathic pulmonary fibrosis (IPF) for discrimination from healthy subjects. Methods: VOCs in the exhaled breath of 40 IPF patients and 55 healthy controls were measured using a multi-capillary column and ion mobility spectrometer. The patients were examined by pulmonary function tests, blood gas analysis, and serum biomarkers of interstitial pneumonia. Results: We detected 85 VOC peaks in the exhaled breath of IPF patients and controls. IPF patients showed 5 significant VOC peaks; p-cymene, acetoin, isoprene, ethylbenzene, and an unknown compound. The VOC peak of p-cymene was significantly lower (p < 0.001), while the VOC peaks of acetoin, isoprene, ethylbenzene, and the unknown compound were significantly higher (p < 0.001 for all) compared with the peaks of controls. Comparing VOC peaks with clinical parameters, negative correlations with VC (r =−0.393, p = 0.013), %VC (r =−0.569, p < 0.001), FVC (r = −0.440, p = 0.004), %FVC (r =−0.539, p < 0.001), DLco (r =−0.394, p = 0.018), and %DLco (r =−0.413, p = 0.008) and a positive correlation with KL-6 (r = 0.432, p = 0.005) were found for p-cymene. Conclusion: We found characteristic 5 VOCs in the exhaled breath of IPF patients. Among them, the VOC peaks of p-cymene were related to the clinical parameters of IPF. These VOCs may be useful biomarkers of IPF.
Social sustainable supply chain management in the textile and apparel industry : a literature review
(2017)
So far, a vast amount of studies on sustainability in supply chain management have been conducted by academics over the last decade. Nevertheless, socially related aspects are still neglected in the related discussion. The primary motivation of the present literature review has arisen from this shortcoming, thus the key purpose of this study is to enrich the discussion by providing a state of-the-art, focusing exclusively on social issues in sustainable supply chain management (SSCM) by considering the textile/apparel sector as the field of application. The authors conduct a literature review, including content analysis which covers 45 articles published in English peer-reviewed journals, and proposes a comprehensive map which integrates the latest findings on socially related practices in the textile/apparel industry with the dominant conceptualization in order to reveal potential research areas in the field. The results show an ongoing lack of investigation regarding the social dimension of the triple bottom line in SSCM. Findings indicate that a company’s internal orientation is the main assisting factor in sustainable supply chain management practices. Further, supplier collaboration and assessment can be interpreted as an offer for suppliers deriving from stakeholders and a focal company’s management of social risk. Nevertheless, suppliers do also face or even create huge barriers in improving their social performance. This calls for more empirical research and qualitative or quantitative survey methods, especially at the supplier level located in developing countries.
Foam has been employed as an improved or enhanced oil recovery method to overcome gravity override and the channeling and fingering of the injected gas, which arises because of the low density and viscosity of the injected fluid combined with the rock heterogeneity. A major challenge, however, is the stability of the generated foam when it contacts the oil. In this study we investigate the feasibility of using inexpensive nanoparticles made of coal fly ash, an abundantly available waste product of coal power plants, as a foam booster. We investigate the viability of reducing the size of fly ash particles to 100−200 nm using high-frequency ultrasonic grinding. We also study the foaminess (foamability), strength, and stability of the foams made with minor concentrations of fly ash nanoparticles and surfactant, both in bulk and porous media. The effect of monovalent and divalent ion concentration on the foaminess of the nanoash suspension combined with very low concentrations of a commercial alpha olefin sulfonate (AOS) surfactant, in the presence and absence of oil, is studied. We observe that bulk foam that contains very small amounts of nanoash particles shows a higher stability in the presence of model oils. Furthermore, experiments in porous media exhibit remarkably stronger foam with mixtures of nanoash and surfactant, such that the amount of produced liquids from the cores significantly increases. For the first time we show that nanoash can be used to stabilize nitrogen foam in the presence of crude oil at high temperature and pressure. In the presence of oil, the nanoash−AOS foam shows a higher stability, although crude oil tends to form stable emulsions in water in the presence of nanoash.
Social networks, smart portable devices, Internet of Things (IoT) on base of technologies like analytics for big data and cloud services are emerging to support flexible connected products and agile services as the new wave of digital transformation. Biological metaphors of living and adaptable ecosystems with service-oriented enterprise architectures provide the foundation for self-optimizing and resilient run-time environments for intelligent business services and related distributed information systems. We are extending Enterprise Architecture (EA) with mechanisms for flexible adaptation and evolution of information systems having distributed IoT and other micro-granular digital architecture to support next digitization products, services, and processes. Our aim is to support flexibility and agile transformation for both IT and business capabilities through adaptive digital enterprise architectures. The present research paper investigates additionally decision mechanisms in the context of multi-perspective explorations of enterprise services and Internet of Things architectures by extending original enterprise architecture reference models with state of art elements for architectural engineering and digitization.
In times of dynamic markets, enterprises have to be agile to be able to quickly react to market influences. Due to the increasing digitization of products, the enterprise IT often is affected when business models change. Enterprise Architecture Management (EAM) targets a holistic view of the enterprise’ IT and their relations to the business. However, Enterprise Architectures (EA) are complex structures consisting of many layers, artifacts and relationships between them. Thus, analyzing EA is a very complex task for stakeholders. Visualizations are common vehicles to support analysis. However, in practice visualization capabilities lack flexibility and interactivity. A solution to improve the support of stakeholders in analyzing EAs might be the application of visual analytics. Starting from a systematic literature review, this article investigates the features of visual analytics relevant for the context of EAM.
The Eighth International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2016), held between June 26 - 30, 2016 - Lisbon, Portugal, continued a series of international events covering a large spectrum of topics related to advances in fundamentals on databases, evolution of relation between databases and other domains, data base technologies and content processing, as well as specifics in applications domains databases. Advances in different technologies and domains related to databases triggered substantial improvements for content processing, information indexing, and data, process and knowledge mining. The push came from Web services, artificial intelligence, and agent technologies, as well as from the generalization of the XML adoption. High-speed communications and computations, large storage capacities, and load-balancing for distributed databases access allow new approaches for content processing with incomplete patterns, advanced ranking algorithms and advanced indexing methods. Evolution on e-business, ehealth and telemedicine, bioinformatics, finance and marketing, geographical positioning systems put pressure on database communities to push the ‘de facto’ methods to support new requirements in terms of scalability, privacy, performance, indexing, and heterogeneity of both content and technology.