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This article covers the design of highly integrated gate drivers and level shifters for high-speed, high power efficiency and dv/dt robustness with focus on automotive applications. With the introduction of the 48 V board net in addition to the conventional 12 V battery, there is an increasing need for fast switching integrated gate drivers in the voltage range of 50 V and above. State-of-the-art drivers are able to switch 50 V in less than 5 ns. The high-voltage electrical drive train demands for galvanic isolated and highly integrated gate drivers. A gate driver with bidirectional signal transmission with a 1 MBit/s amplitude modulation, 10/20 MHz frequency modulation and power transfer over one single transformer will be discussed. The concept of high-voltage charge storing enables an area-efficient fully integrated bootstrapping supply with 70 % less area consumption. EMC is a major concern in automotive. Gate drivers with slope control optimize EMC while maintaining good switching efficiency. A current mode gate driver, which can change its drive current within 10 ns, results in 20 dBuV lower emissions between 7 and 60 MHz and 52 % lower switching loss compared to a conventional constant current gate driver.
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.
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.
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.
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.
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 .
To prevent high buildings in endangered zones suffering from seismic attack, TMD are applied successfully. In many applications the dampers are placed along the height of the edifice to reduce the damage during the earthquake. The dimensioning of TMD is a multidimensional optimisation problem with many local maxima. To find the absolute best or a very good design, advanced optimisation strategies have to be applied. Bionic optimization proposes different methods to deal with such tasks but requires many repeated studies of the buildings and dampers design. To improve the speed of the analysis, the authors propose a reduced model of the building including the dampers. A series of consecutive generations shows a growing capacity to reduce the impact of an earthquake on the building. The proposals found help to dimension the dampers. A detailed analysis of the building under earthquake loading may yield an efficient design.
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.
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.
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.