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Instead of waiting for and constantly adapting to details of political interventions, utilities need to focus on their environment from a holistic perspective. The unique position of the company - be it a local utility, a bigger player, or an international utility specializing in specitic segments - has to be the basis of goals and strategies. But without consistent translation of these goals and strategies into processes, structures, and company culture, a strategy remains pure theory. Companies need to engage in a continuing learning process. This means being willing to pass on strategies, to slow down or speed up, to work from a different angle etc.
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.
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.
Virtual prototyping of integrated mixed-signal smart sensor systems requires high-performance co-simulation of analog frontend circuitry with complex digital controller hardware and embedded real-time software. We use SystemC/TLM 2.0 in conjunction with a cycle-count accurate temporal decoupling approach (TD) to simulate digital components and firmware code execution at high speed while preserving clock-cycle accuracy and, thus, real-time behavior at time quantum boundaries. Optimal time quanta ensuring real-time capability can be calculated and set automatically during simulation if the simulation engine has access to exact timing information about upcoming inter-process communication events. These methods fail in the case of non-deterministic, asynchronous events, resulting in potentially invalid simulation results. In this paper, we propose an extension to the case of asynchronous events generated by blackbox sources from which a priori event timing information is not available, such as coupled analog simulators or hardware in the loop. Additional event processing latency or rollback effort caused by temporal decoupling is minimized by calculating optimal time quanta dynamically in a SystemC model using a linear prediction scheme. We analyze the theoretical performance of the presented predictive temporal decoupling approach (PTD) by deriving a cost model that expresses the expected simulation effort in terms of key parameters such as time quantum size and CPU time per simulation cycle. For an exemplary smart-sensor system model, we show that quasi-periodic events that trigger activities in TD processes are handled accurately after the predictor has settled.