Refine
Year of publication
- 2019 (309) (remove)
Institute
- Fachbereich Medizintechnik und Technomathematik (70)
- IfB - Institut für Bioengineering (45)
- Fachbereich Elektrotechnik und Informationstechnik (44)
- Fachbereich Luft- und Raumfahrttechnik (44)
- Fachbereich Wirtschaftswissenschaften (34)
- Fachbereich Bauingenieurwesen (28)
- Fachbereich Energietechnik (27)
- Fachbereich Maschinenbau und Mechatronik (26)
- INB - Institut für Nano- und Biotechnologien (19)
- Fachbereich Chemie und Biotechnologie (16)
Has Fulltext
- no (309) (remove)
Document Type
- Article (121)
- Conference Proceeding (93)
- Part of a Book (43)
- Book (24)
- Other (9)
- Doctoral Thesis (5)
- Patent (5)
- Review (4)
- Conference: Meeting Abstract (2)
- Examination Thesis (1)
Keywords
- Digitalisierung (3)
- Datenschutz (2)
- Enterprise Architecture (2)
- Robotic Process Automation (2)
- Seismic design (2)
- Achilles tendon (1)
- Advanced driver assistance systems (ADAS/AD) (1)
- Aircraft design (1)
- Analytics (1)
- Arbeit 4.0 (1)
Water suppliers are faced with the great challenge of achieving high-quality and, at the same time, low-cost water supply. In practice, the focus is set on the most beneficial maintenance measures and/or capacity adaptations of existing water distribution systems (WDS). Since climatic and demographic influences will pose further challenges in the future, the resilience enhancement of WDS, i.e. the enhancement of their capability to withstand and recover from disturbances, has been in particular focus recently. To assess the resilience of WDS, metrics based on graph theory have been proposed. In this study, a promising approach is applied to assess the resilience of the WDS for a district in a major German City. The conducted analysis provides insight into the process of actively influencing the
resilience of WDS
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.