@book{Altherr2016,
  author    = {Altherr, Lena},
  title     = {Algorithmic System Design under Consideration of Dynamic Processes},
  publisher = {Shaker},
  address   = {Aachen},
  isbn      = {978-3-8440-4848-3},
  pages     = {94},
  year      = {2016},
  abstract  = {Nach Stand von Wissenschaft und Technik werden Komponenten hinsichtlich ihrer Eigenschaften, wie Lebensdauer oder Energieeffizienz, optimiert. Allerdings k{\"o}nnen selbst hervorragende Komponenten zu ineffizienten oder instabilen Systemen f{\"u}hren, wenn ihr Zusammenspiel nur unzureichend ber{\"u}cksichtigt wird. Eine Systembetrachtung schafft ein gr{\"o}ßeres Optimierungspotential - dem erh{\"o}hten Potential steht jedoch auch ein erh{\"o}hter Komplexit{\"a}tsgrad gegen{\"u}ber. Die vorliegende Arbeit ist im Rahmen des Sonderforschungsbereichs 805 entstanden, dessen Ziel die Beherrschung von Unsicherheit in Systemen des Maschinenbaus ist. Die Arbeit zeigt anhand eines realen Systems aus dem Bereich der Hydraulik, wie Unsicherheit in der Entwicklungsphase beherrscht werden kann. Hierbei ist neu, dass die durch den sp{\"a}teren Betrieb zu erwartende Systemdegradation eines jeden m{\"o}glichen Systemvorschlags antizipiert werden kann. Dadurch k{\"o}nnen Betriebs- und Wartungskosten vorausgesagt und minimiert werden und durch eine optimale Betriebs- und Wartungsstrategie die Verf{\"u}gbarkeit des Systems garantiert werden. Wesentliche Fragen bei der optimalen Auslegung des betrachteten hydrostatischen Getriebes sind dessen physikalische Modellierung, die Darstellung des Optimierungsproblems als gemischt-ganzzahliges lineares Programm, und dessen algorithmische Behandlung zur L{\"o}sungsfindung. Hierzu werden Heuristiken zum schnelleren Auffinden sinnvoller Systemtopologien vorgestellt und mittels mathematischer Dekomposition eine Bewertung des dynamischen Verschleiß- und Wartungsverlaufs m{\"o}glicher Systemvorschl{\"a}ge vorgenommen. Die Arbeit stellt die Optimierung technischer Systeme an der Schnittstelle von Mathematik, Informatik und Ingenieurwesen sowohl gr{\"u}ndlich als auch anschaulich und nachvollziehbar dar.},
  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}
}
@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}
}