@inproceedings{MuellerSchmittLeiseetal.2021,
  author    = {M{\"u}ller, Tim M. and Schmitt, Andreas and Leise, Philipp and Meck, Tobias and Altherr, Lena and Pelz, Peter F. and Pfetsch, Marc E.},
  title     = {Validation of an optimized resilient water supply system},
  series = {Uncertainty in Mechanical Engineering},
  booktitle = {Uncertainty in Mechanical Engineering},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-77255-0},
  doi       = {10.1007/978-3-030-77256-7_7},
  pages     = {70 -- 80},
  year      = {2021},
  abstract  = {Component failures within water supply systems can lead to significant performance losses. One way to address these losses is the explicit anticipation of failures within the design process. We consider a water supply system for high-rise buildings, where pump failures are the most likely failure scenarios. We explicitly consider these failures within an early design stage which leads to a more resilient system, i.e., a system which is able to operate under a predefined number of arbitrary pump failures. We use a mathematical optimization approach to compute such a resilient design. This is based on a multi-stage model for topology optimization, which can be described by a system of nonlinear inequalities and integrality constraints. Such a model has to be both computationally tractable and to represent the real-world system accurately. We therefore validate the algorithmic solutions using experiments on a scaled test rig for high-rise buildings. The test rig allows for an arbitrary connection of pumps to reproduce scaled versions of booster station designs for high-rise buildings. We experimentally verify the applicability of the presented optimization model and that the proposed resilience properties are also fulfilled in real systems.},
  language  = {en}
}
@incollection{LeiseAltherrSimonetal.2019,
  author    = {Leise, Philipp and Altherr, Lena and Simon, Nicolai and Pelz, Peter F.},
  title     = {Finding global-optimal gearbox designs for battery electric vehicles},
  series = {Optimization of complex systems - theory, models, algorithms and applications : WCGO 2019},
  booktitle = {Optimization of complex systems - theory, models, algorithms and applications : WCGO 2019},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-21802-7},
  doi       = {10.1007/978-3-030-21803-4_91},
  pages     = {916 -- 925},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}