@inproceedings{AltherrEdererFarnetaneetal.2017, author = {Altherr, Lena and Ederer, Thorsten and Farnetane, Lucas S. and P{\"o}ttgen, Philipp and Verg{\´e}, Angela and Pelz, Peter F.}, title = {Multicriterial design of a hydrostatic transmission system via mixed-integer programming}, series = {Operations Research Proceedings 2015}, booktitle = {Operations Research Proceedings 2015}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-42901-4}, doi = {10.1007/978-3-319-42902-1_41}, pages = {301 -- 307}, year = {2017}, abstract = {In times of planned obsolescence the demand for sustainability keeps growing. Ideally, a technical system is highly reliable, without failures and down times due to fast wear of single components. At the same time, maintenance should preferably be limited to pre-defined time intervals. Dispersion of load between multiple components can increase a system's reliability and thus its availability inbetween maintenance points. However, this also results in higher investment costs and additional efforts due to higher complexity. Given a specific load profile and resulting wear of components, it is often unclear which system structure is the optimal one. Technical Operations Research (TOR) finds an optimal structure balancing availability and effort. We present our approach by designing a hydrostatic transmission system.}, language = {en} } @incollection{AltherrEdererLorenzetal.2016, author = {Altherr, Lena and Ederer, Thorsten and Lorenz, Ulf and Pelz, Peter F. and P{\"o}ttgen, Philipp}, title = {Designing a feedback control system via mixed-integer programming}, series = {Operations Research Proceedings 2014: Selected Papers of the Annual International Conference of the German Operations Research}, booktitle = {Operations Research Proceedings 2014: Selected Papers of the Annual International Conference of the German Operations Research}, editor = {L{\"u}bbecke, Marco E. and Koster, Arie and Letmathe, Peter and Madlener, Reihard and Preis, Britta and Walther, Grit}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-28695-2}, doi = {10.1007/978-3-319-28697-6_18}, pages = {121 -- 127}, year = {2016}, abstract = {Pure analytical or experimental methods can only find a control strategy for technical systems with a fixed setup. In former contributions we presented an approach that simultaneously finds the optimal topology and the optimal open-loop control of a system via Mixed Integer Linear Programming (MILP). In order to extend this approach by a closed-loop control we present a Mixed Integer Program for a time discretized tank level control. This model is the basis for an extension by combinatorial decisions and thus for the variation of the network topology. Furthermore, one is able to appraise feasible solutions using the global optimality gap.}, language = {en} } @article{AltherrEdererLorenzetal.2014, author = {Altherr, Lena and Ederer, Thorsten and Lorenz, Ulf and Pelz, Peter F. and P{\"o}ttgen, Philipp}, title = {Experimental validation of an enhanced system synthesis approach}, series = {Operations Research Proceedings 2014}, journal = {Operations Research Proceedings 2014}, editor = {L{\"u}bbecke, Marco and Koster, Arie and Letmathe, Peter and Madlener, Reihard and Peis, Britta and Walther, Grit}, publisher = {Springer}, address = {Basel}, isbn = {978-3-319-28695-2}, doi = {10.1007/978-3-319-28697-6_1}, pages = {6}, year = {2014}, abstract = {Planning the layout and operation of a technical system is a common task for an engineer. Typically, the workflow is divided into consecutive stages: First, the engineer designs the layout of the system, with the help of his experience or of heuristic methods. Secondly, he finds a control strategy which is often optimized by simulation. This usually results in a good operating of an unquestioned sys- tem topology. In contrast, we apply Operations Research (OR) methods to find a cost-optimal solution for both stages simultaneously via mixed integer program- ming (MILP). Technical Operations Research (TOR) allows one to find a provable global optimal solution within the model formulation. However, the modeling error due to the abstraction of physical reality remains unknown. We address this ubiq- uitous problem of OR methods by comparing our computational results with mea- surements in a test rig. For a practical test case we compute a topology and control strategy via MILP and verify that the objectives are met up to a deviation of 8.7\%.}, language = {en} } @article{AltherrEdererPoettgenetal.2015, author = {Altherr, Lena and Ederer, Thorsten and P{\"o}ttgen, Philipp and Lorenz, Ulf and Pelz, Peter F.}, title = {Multicriterial optimization of technical systems considering multiple load and availability scenarios}, series = {Applied Mechanics and Materials}, volume = {807}, journal = {Applied Mechanics and Materials}, editor = {Pelz, Peter F. and Groche, Peter}, isbn = {1660-9336}, doi = {10.4028/www.scientific.net/AMM.807.247}, pages = {247 -- 256}, year = {2015}, abstract = {Cheap does not imply cost-effective -- this is rule number one of zeitgeisty system design. The initial investment accounts only for a small portion of the lifecycle costs of a technical system. In fluid systems, about ninety percent of the total costs are caused by other factors like power consumption and maintenance. With modern optimization methods, it is already possible to plan an optimal technical system considering multiple objectives. In this paper, we focus on an often neglected contribution to the lifecycle costs: downtime costs due to spontaneous failures. Consequently, availability becomes an issue.}, language = {en} } @inproceedings{AltherrEdererSchaenzleetal.2017, author = {Altherr, Lena and Ederer, Thorsten and Sch{\"a}nzle, Christian and Lorenz, Ulf and Pelz, Peter F.}, title = {Algorithmic system design using scaling and affinity laws}, series = {Operations Research Proceedings 2015}, booktitle = {Operations Research Proceedings 2015}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-42901-4}, doi = {10.1007/978-3-319-42902-1}, pages = {605 -- 611}, year = {2017}, abstract = {Energy-efficient components do not automatically lead to energy-efficient systems. Technical Operations Research (TOR) shifts the focus from the single component to the system as a whole and finds its optimal topology and operating strategy simultaneously. In previous works, we provided a preselected construction kit of suitable components for the algorithm. This approach may give rise to a combinatorial explosion if the preselection cannot be cut down to a reasonable number by human intuition. To reduce the number of discrete decisions, we integrate laws derived from similarity theory into the optimization model. Since the physical characteristics of a production series are similar, it can be described by affinity and scaling laws. Making use of these laws, our construction kit can be modeled more efficiently: Instead of a preselection of components, it now encompasses whole model ranges. This allows us to significantly increase the number of possible set-ups in our model. In this paper, we present how to embed this new formulation into a mixed-integer program and assess the run time via benchmarks. We present our approach on the example of a ventilation system design problem.}, language = {en} } @inproceedings{LeiseAltherrPelz2018, author = {Leise, Philipp and Altherr, Lena and Pelz, Peter F.}, title = {Technical Operations Research (TOR) - Algorithms, not Engineers, Design Optimal Energy Efficient and Resilient Cooling Systems}, series = {FAN2018 - Proceedings of the International Conference on Fan Noise, Aerodynamics, Applications and Systems}, booktitle = {FAN2018 - Proceedings of the International Conference on Fan Noise, Aerodynamics, Applications and Systems}, pages = {1 -- 12}, year = {2018}, abstract = {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.}, language = {en} } @incollection{LeiseAltherrPelz2018, author = {Leise, Philipp and Altherr, Lena and Pelz, Peter F.}, title = {Energy-Efficient design of a water supply system for skyscrapers by mixed-integer nonlinear programming}, series = {Operations Research Proceedings 2017}, booktitle = {Operations Research Proceedings 2017}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-89919-0}, doi = {10.1007/978-3-319-89920-6_63}, year = {2018}, abstract = {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.}, 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{LeiseBreuerAltherretal.2020, author = {Leise, Philipp and Breuer, Tim and Altherr, Lena and Pelz, Peter F.}, title = {Development, validation and assessment of a resilient pumping system}, series = {Proceedings of the Joint International Resilience Conference, JIRC2020}, booktitle = {Proceedings of the Joint International Resilience Conference, JIRC2020}, isbn = {978-90-365-5095-6}, pages = {97 -- 100}, year = {2020}, abstract = {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.}, language = {en} } @article{LeiseEsserEichenlaubetal.2021, author = {Leise, Philipp and Eßer, Arved and Eichenlaub, Tobias and Schleiffer, Jean-Eric and Altherr, Lena and Rinderknecht, Stephan and Pelz, Peter F.}, title = {Sustainable system design of electric powertrains - comparison of optimization methods}, series = {Engineering Optimization}, journal = {Engineering Optimization}, publisher = {Taylor \& Francis}, address = {London}, issn = {0305-215X}, doi = {10.1080/0305215X.2021.1928660}, year = {2021}, abstract = {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.}, language = {en} }