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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 .
Automated stabilization of loading capacity of coal shearer screw with controlled cutting drive
(2015)
A solution of topical scientific problem of coal shearer output increase providing minimum specific power supply for coal cutting, transportation, and loading in terms of thin seams has been proposed. The solution is based on the use of earlier proposed criterion of screw gumming for optimum cutting velocity-coal shearer feed rate ratio in the context of increased screw rotation owing to phase voltage frequency increase. Simulation results of automated control system for coal shearer operations with frequency-controlled cutting drive within thin seams have confirmed the efficiency of the system using proposed algorithm of smart analysis of coal shearer power signal.
Silicon neurons represent different levels of biological details and accuracies as a trade-off between complexity and power consumption. With respect to this trade-off and high similarity to neuron behaviour models, relaxation-type oscillator circuits often yield a good compromise to emulate neurons. In this chapter, two exemplified relaxation-type silicon neurons are presented that emulate neural behaviour with energy consumption under the scale of nJ/spike. The first proposed fully CMOS relaxation SiN is based on mathematical Izhikevich model and can mimic a broad range of physiologically observable spike patterns. The results of kinds of biologically plausible output patterns and coupling process of two SiNs are presented in 0.35 μm CMOS technology. The second type is a novel ultra-low-frequency hybrid CMOS-memristive SiN based on relaxation oscillators and analog memristive devices. The hybrid SiN directly emulates neuron behaviour in the range of physiological spiking frequencies (less than 100 Hz). The relaxation oscillator is implemented and fabricated in 0.13 μm CMOS technology. An autonomous neuronal synchronization process is demonstrated with two relaxation oscillators coupled by an analog memristive device in the measurement to emulate the synchronous behaviour between spiking neurons.
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.
Der Entwurf analoger integrierter Schaltkreise ist bis heute durch einen weitgehend manuellen Entwurfsstil mit anschließender Verifikation gekennzeichnet. Das Backend dieses Prozesses bildet der Layoutentwurf, der mit der SDL-Methode (schematic driven layout) durchgeführt und mit den Verifikationsschritten DRC und LVS abgeschlossen wird. Als Ziel wird i.a. in Analogie zu den im Digitalbereich existierenden Lösungen eine vollautomatische Layoutsynthese auch für Analogschaltungen angestrebt. Die hier vorgeschlagene neue Designmethodik hat nicht diese vielfach geforderte Layoutsynthese im Analogbereich zum Inhalt. Sie stellt vielmehr einen realistischeren - und aus Sicht des Autors vor allem notwendigen - Zwischenschritt dar. Die Kernaussage besteht darin, dass zunächst eine Methode bereitzustellen ist, bei der alle die Schaltungsfunktion beeinflussenden Randbedingungen (constraints) rechnergestützt prüfbar sein müssen. Erst auf dieser Basis wird es gelingen, in einem weiteren Schritt analoges Layout zu synthetisieren. Diese These wird aus einer Betrachtung der historischen Entwicklung der EDA-Werkzeuge hergeleitet. Die Extrapolation dieser Historie lässt eine Wegskizze für einen neuen "constraint-driven" Designflow erkennen, dessen Hauptvorteil in einer rechnergestützten Absicherung der Schaltungsfunktion besteht. Weitere mögliche neue Merkmale eines solchen Designflows werden diskutiert: Abkehr von den klassischen sequentiellen Designschritten wie Platzierung und Routing hin zu einer "kontinuierlichen" Layoutentstehung und neuartige Chancen für eine wesentlich verbesserte Wiederverwendbarkeit (reuse) von Layoutergebnissen durch die Nutzung höherer Abstraktionsebenen.
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.
The generous feed-in tariffs (FiTs) introduced in Germany—which resulted in major growth in decentralized solar photovoltaic (PV) systems—will phase out in the coming years, making many of the existing distributed generation assets stranded. This challenge creates an opportunity for community-focused energy utilities, such as Elektrizitätswerke Schönau eG (EWS) based in Schönau, Germany, to try a new approach to assist its customers, makes the transition to a more sustainable future. This chapter describes how EWS is developing products and offering community-based solutions including peer-to-peer trading using automated platforms. Such innovative offering may lead to successful differentiation in a competitive and highly decentralized future.
Die Analyse der geometrischen Parameter der Werkzeuge und der kinematisch bedingten Eingriffsverhältnisse beim Fräsen führen zu einer erheblichen Beeinflussung der Schneidenbelastungen während des Einsatzes. Eine exzentrische Aufnahme von Schaftwerkzeugen bedeutet eine deutliche Belastung der exzentrischen Schneiden. Diese Belastung liegt deutlich über der durch die Ungleichteilung erzeugten Kraftmodulation. Weiterhin werden durch die Impulsbelastung der Schneideneintritte die Resonanzen der Struktur angeregt. Dies beeinflusst zum einen die Messungen mit der Kraftmessplattform. Zum anderen werden während der realen Bearbeitung durch diese Wechselwirkung die Oberflächen der bearbeiteten Bauteile beeinflusst.
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.