TY - BOOK A1 - Lepers, Heinrich T1 - SPS-Programmierung nach IEC 61131-3 : mit Beispielen für CoDeSys und STEP 7 ; [auf 4 DVDs/CDs: Installationssoftware für CoDeSys, STEP 7 und WINCC flexible ; alle Beispiele aus dem Buch für CoDeSys und STEP7 ; alle Lösungen zu den Übungsprojekten für CoDeSys und STEP 7] / Heinrich Lepers . 3. neu bearb. Aufl. Y1 - 2009 SN - 978-3-7723-5806-7 PB - Franzis CY - Poing ER - TY - CHAP A1 - Lenz, Laura L. A1 - Wolf, Martin ED - Kritz, Willy Christian T1 - Economic evaluation of serious games with the comparative assessment framework COSEGA T2 - The shift from teaching to learning : individual, collective and organizational learning through gaming simulation ; proceedings of the 45th conference of the International Simulation and Gaming Association, Dornbirn 2014 Y1 - 2014 SN - 978-3-7639-5422-3 SP - 374 EP - 386 PB - Bertelsmann CY - [Bielefeld] ER - TY - CHAP A1 - Leise, Philipp A1 - Simon, Nicolai A1 - Altherr, Lena T1 - Comparison of Piecewise Linearization Techniques to Model Electric Motor Efficiency Maps: A Computational Study T2 - Operations Research Proceedings 2019 N2 - To maximize the travel distances of battery electric vehicles such as cars or buses for a given amount of stored energy, their powertrains are optimized energetically. One key part within optimization models for electric powertrains is the efficiency map of the electric motor. The underlying function is usually highly nonlinear and nonconvex and leads to major challenges within a global optimization process. To enable faster solution times, one possibility is the usage of piecewise linearization techniques to approximate the nonlinear efficiency map with linear constraints. Therefore, we evaluate the influence of different piecewise linearization modeling techniques on the overall solution process and compare the solution time and accuracy for methods with and without explicitly used binary variables. KW - MINLP KW - Powertrain KW - Piecewise linearization KW - Efficiency optimization Y1 - 2020 SN - 978-3-030-48439-2 SN - 978-3-030-48438-5 U6 - http://dx.doi.org/10.1007/978-3-030-48439-2_55 N1 - Annual International Conference of the German Operations Research Society (GOR), Dresden, Germany, September 4-6, 2019 SP - 457 EP - 463 PB - Springer CY - Cham ER - TY - JOUR A1 - Leise, Philipp A1 - Eßer, Arved A1 - Eichenlaub, Tobias A1 - Schleiffer, Jean-Eric A1 - Altherr, Lena A1 - Rinderknecht, Stephan A1 - Pelz, Peter F. T1 - Sustainable system design of electric powertrains - comparison of optimization methods JF - Engineering Optimization N2 - The transition within transportation towards battery electric vehicles can lead to a more sustainable future. To account for the development goal ‘climate action’ stated by the United Nations, it is mandatory, within the conceptual design phase, to derive energy-efficient system designs. One barrier is the uncertainty of the driving behaviour within the usage phase. This uncertainty is often addressed by using a stochastic synthesis process to derive representative driving cycles and by using cycle-based optimization. To deal with this uncertainty, a new approach based on a stochastic optimization program is presented. This leads to an optimization model that is solved with an exact solver. It is compared to a system design approach based on driving cycles and a genetic algorithm solver. Both approaches are applied to find efficient electric powertrains with fixed-speed and multi-speed transmissions. Hence, the similarities, differences and respective advantages of each optimization procedure are discussed. KW - Powertrain KW - stochastic optimization KW - global optimization KW - genetic algorithm Y1 - 2021 U6 - http://dx.doi.org/10.1080/0305215X.2021.1928660 SN - 0305-215X PB - Taylor & Francis CY - London ER - TY - CHAP A1 - Leise, Philipp A1 - Breuer, Tim A1 - Altherr, Lena A1 - Pelz, Peter F. T1 - Development, validation and assessment of a resilient pumping system T2 - Proceedings of the Joint International Resilience Conference, JIRC2020 N2 - The development of resilient technical systems is a challenging task, as the system should adapt automatically to unknown disturbances and component failures. To evaluate different approaches for deriving resilient technical system designs, we developed a modular test rig that is based on a pumping system. On the basis of this example system, we present metrics to quantify resilience and an algorithmic approach to improve resilience. This approach enables the pumping system to automatically react on unknown disturbances and to reduce the impact of component failures. In this case, the system is able to automatically adapt its topology by activating additional valves. This enables the system to still reach a minimum performance, even in case of failures. Furthermore, timedependent disturbances are evaluated continuously, deviations from the original state are automatically detected and anticipated in the future. This allows to reduce the impact of future disturbances and leads to a more resilient system behaviour. KW - water supply system KW - fault detection KW - anticipation strategy Y1 - 2020 SN - 978-90-365-5095-6 N1 - Proceedings of the Joint International Resilience Conference 2020. Interconnected: Resilience Innovations for Sustainable Development Goals. 23 - 27 November, 2020 SP - 97 EP - 100 ER - TY - CHAP A1 - Leise, Philipp A1 - Altherr, Lena A1 - Simon, Nicolai A1 - Pelz, Peter F. T1 - Finding global-optimal gearbox designs for battery electric vehicles T2 - Optimization of complex systems - theory, models, algorithms and applications : WCGO 2019 N2 - 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. KW - Powertrain KW - Gearbox KW - Optimization KW - BEV KW - WLTP Y1 - 2019 SN - 978-3-030-21802-7 U6 - http://dx.doi.org/10.1007/978-3-030-21803-4_91 SP - 916 EP - 925 PB - Springer CY - Cham ER - TY - CHAP A1 - Leise, Philipp A1 - Altherr, Lena A1 - Pelz, Peter F. T1 - Technical Operations Research (TOR) - Algorithms, not Engineers, Design Optimal Energy Efficient and Resilient Cooling Systems T2 - FAN2018 - Proceedings of the International Conference on Fan Noise, Aerodynamics, Applications and Systems N2 - The overall energy efficiency of ventilation systems can be improved by considering not only single components, but by considering as well the interplay between every part of the system. With the help of the method "TOR" ("Technical Operations Research"), which was developed at the Chair of Fluid Systems at TU Darmstadt, it is possible to improve the energy efficiency of the whole system by considering all possible design choices programmatically. We show the ability of this systematic design approach with a ventilation system for buildings as a use case example. Based on a Mixed-Integer Nonlinear Program (MINLP) we model the ventilation system. We use binary variables to model the selection of different pipe diameters. Multiple fans are model with the help of scaling laws. The whole system is represented by a graph, where the edges represent the pipes and fans and the nodes represents the source of air for cooling and the sinks, that have to be cooled. At the beginning, the human designer chooses a construction kit of different suitable fans and pipes of different diameters and different load cases. These boundary conditions define a variety of different possible system topologies. It is not possible to consider all topologies by hand. With the help of state of the art solvers, on the other side, it is possible to solve this MINLP. Next to this, we also consider the effects of malfunctions in different components. Therefore, we show a first approach to measure the resilience of the shown example use case. Further, we compare the conventional approach with designs that are more resilient. These more resilient designs are derived by extending the before mentioned model with further constraints, that consider explicitly the resilience of the overall system. We show that it is possible to design resilient systems with this method already in the early design stage and compare the energy efficiency and resilience of these different system designs. Y1 - 2018 N1 - International Conference on Fan Noise, Aerodynamics, Applications and Systems 18-20.04.2018 Darmstadt, Deutschland SP - 1 EP - 12 ER - TY - CHAP A1 - Leise, Philipp A1 - Altherr, Lena A1 - Pelz, Peter F. T1 - Energy-Efficient design of a water supply system for skyscrapers by mixed-integer nonlinear programming T2 - Operations Research Proceedings 2017 N2 - The energy-efficiency of technical systems can be improved by a systematic design approach. Technical Operations Research (TOR) employs methods known from Operations Research to find a global optimal layout and operation strategy of technical systems. We show the practical usage of this approach by the systematic design of a decentralized water supply system for skyscrapers. All possible network options and operation strategies are modeled by a Mixed-Integer Nonlinear Program. We present the optimal system found by our approach and highlight the energy savings compared to a conventional system design. KW - Engineering optimization KW - Global optimization KW - Energy efficiency KW - Water KW - Network Y1 - 2018 SN - 978-3-319-89919-0 U6 - http://dx.doi.org/10.1007/978-3-319-89920-6_63 PB - Springer CY - Cham ER - TY - CHAP A1 - Leise, Philipp A1 - Altherr, Lena T1 - Optimizing the design and control of decentralized water supply systems – a case-study of a hotel building T2 - EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization N2 - To increase pressure to supply all floors of high buildings with water, booster stations, normally consisting of several parallel pumps in the basement, are used. In this work, we demonstrate the potential of a decentralized pump topology regarding energy savings in water supply systems of skyscrapers. We present an approach, based on Mixed-Integer Nonlinear Programming, that allows to choose an optimal network topology and optimal pumps from a predefined construction kit comprising different pump types. Using domain-specific scaling laws and Latin Hypercube Sampling, we generate different input sets of pump types and compare their impact on the efficiency and cost of the total system design. As a realistic application example, we consider a hotel building with 325 rooms, 12 floors and up to four pressure zones. KW - Engineering optimization KW - Energy efficiency KW - Water KW - Pump System KW - Latin Hypercube Sampling Y1 - 2018 SN - 978-3-319-97773-7 SN - 978-3-319-97772-0 U6 - http://dx.doi.org/10.1007/978-3-319-97773-7_107 N1 - EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization. 17-19 September 2018. Lisboa, Portugal SP - 1241 EP - 1252 PB - Springer CY - Cham ER - TY - CHAP A1 - Leise, Philipp A1 - Altherr, Lena T1 - Experimental evaluation of resilience metrics in a fluid system T2 - Mastering Uncertainty in Mechanical Engineering Y1 - 2021 SN - 978-3-030-78356-3 N1 - Unterkapitel des Kapitels "Strategies for Mastering Uncertainty" SP - 442 EP - 447 PB - Springer CY - Cham ER -