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In der Diskussion über die Digitalisierung der Forschung spielt die Frage nach der optimalen IT-Unterstützung für Forschende eine wichtige Rolle. Forschende können heute an ihren Hochschulen bzw. Wissenschaftseinrichtungen auf ein breites Angebot interner IT-Dienstleistungen zurückgreifen, das auch kooperative IT-Dienste umfasst, die von mehreren Institutionen in Zusammenarbeit bereitgestellt werden. Außerhalb der eigenen Organisation und des weiteren Verbunds hat sich im Internet zudem ein breites externes Angebot an innovativen, häufig kostenlos nutzbaren Onlinediensten entwickelt. Neben horizontalen Onlinediensten, die sich prinzipiell an jeden Internetnutzer richten (bspw. Dropbox, Twitter, WhatsApp), nimmt auch die Zahl von vertikalen Diensten für wissenschaftliche bzw. Forschungszwecke immer weiter zu (bspw. GoogleScholar, ResearchGate, figshare). Für Forschende eröffnen sich damit vielfältige neue Möglichkeiten, ihren individuellen Forschungsprozess durch digitale Werkzeuge zu verbessern. Aufgrund rechtlicher, technischer und personeller Restriktionen können jedoch interne Dienstleister bei der Identifizierung, Auswahl und Nutzung externer Onlinedienste nur wenig Unterstützung leisten. Aus einer serviceorientierten Perspektive stehen Forschende zunehmend vor dem Problem, wie sich heterogene IT-Dienste interner und externer Anbieter in den eigenen Forschungsprozess integrieren lassen. Als Lösungsansatz skizziert das Kapitel das Konzept eines persönlichen Forschungsinformationssystems
nach Gesichtspunkten eines digitalen Servicesystems.
Betrifft Design : von historischen Zukunftsperspektiven zu gegenwärtigen Vergangenheitssehnsüchten
(2019)
In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead.
In order to maximize the possible travel distance of battery electric vehicles with one battery charge, it is mandatory to adjust all components of the powertrain carefully to each other. While current vehicle designs mostly simplify the powertrain rigorously and use an electric motor in combination with a gearbox with only one fixed transmission ratio, the use of multi-gear systems has great potential. First, a multi-speed system is able to improve the overall energy efficiency. Secondly, it is able to reduce the maximum momentum and therefore to reduce the maximum current provided by the traction battery, which results in a longer battery lifetime. In this paper, we present a systematic way to generate multi-gear gearbox designs that—combined with a certain electric motor—lead to the most efficient fulfillment of predefined load scenarios and are at the same time robust to uncertainties in the load. Therefore, we model the electric motor and the gearbox within a Mixed-Integer Nonlinear Program, and optimize the efficiency of the mechanical parts of the powertrain. By combining this mathematical optimization program with an unsupervised machine learning algorithm, we are able to derive global-optimal gearbox designs for practically relevant momentum and speed requirements.
Rapid Tooling
(2019)
The chapter initially provides a summary of the contents of Eurocode 8, its aim being to offer both to the students and to practising engineers an easy introduction into the calculation and dimensioning procedures of this earthquake code. Specifically, the general rules for earthquake-resistant structures, the definition of design response spectra taking behaviour and importance factors into account, the application of linear and non-linear calculation methods and the structural safety verifications at the serviceability and ultimate limit state are presented. The application of linear and non-linear calculation methods and corresponding seismic design rules is demonstrated on practical examples for reinforced concrete, steel and masonry buildings. Furthermore, the seismic assessment of existing buildings is discussed and illustrated on the example of a typical historical masonry building in Italy. The examples are worked out in detail and each step of the design process, from the preliminary analysis to the final design, is explained in detail.