TY - CHAP A1 - Altherr, Lena A1 - Ederer, Thorsten A1 - Schänzle, Christian A1 - Lorenz, Ulf A1 - Pelz, Peter F. T1 - Algorithmic system design using scaling and affinity laws T2 - Operations Research Proceedings 2015 N2 - 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. KW - Optimal Topology KW - Piecewise Linearization KW - Ventilation System KW - Similarity Theory Y1 - 2017 SN - 978-3-319-42901-4 SN - 978-3-319-42902-1 U6 - http://dx.doi.org/10.1007/978-3-319-42902-1 N1 - International Conference of the German, Austrian and Swiss Operations Research Societies (GOR, ÖGOR, SVOR/ASRO), University of Vienna, Austria, September 1-4, 2015 SP - 605 EP - 611 PB - Springer CY - Cham ER - TY - CHAP A1 - Nikolovski, Gjorgji A1 - Reke, Michael A1 - Elsen, Ingo A1 - Schiffer, Stefan T1 - Machine learning based 3D object detection for navigation in unstructured environments T2 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) N2 - In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine. KW - 3D object detection KW - LiDAR KW - autonomous driving KW - Deep learning KW - Three-dimensional displays Y1 - 2021 SN - 978-1-6654-7921-9 U6 - http://dx.doi.org/10.1109/IVWorkshops54471.2021.9669218 N1 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 11-17 July 2021, Nagoya, Japan. SP - 236 EP - 242 PB - IEEE ER - TY - CHAP A1 - Altherr, Lena A1 - Ederer, Thorsten A1 - Vergé, Angela A1 - Pelz, Peter F. T1 - Algorithmische Struktursynthese eines hydrostatischen Getriebes T2 - Antriebssysteme 2015 : Elektrik, Mechanik, Fluidtechnik in der Anwendung Y1 - 2015 SN - 978-3-18-092268-3 N1 - Antriebssysteme 2015 - Elektrik, Mechanik, Fluidtechnik in der Anwendung. VDI/VDE-Fachtagung. 11.11.15-12.11.15, Aachen. Veröffentlicht in der Reihe VDI-Berichte, Bandnummer 2268. SP - 145 EP - 155 PB - VDI-Verlag CY - Düsseldorf ER - TY - CHAP A1 - Schänzle, Christian A1 - Altherr, Lena A1 - Ederer, Thorsten A1 - Lorenz, Ulf A1 - Pelz, Peter F. T1 - As good as it can be: Ventilation system design by a combined scaling and discrete optimization method T2 - Proceedings of FAN 2015 N2 - The understanding that optimized components do not automatically lead to energy-efficient systems sets the attention from the single component on the entire technical system. At TU Darmstadt, a new field of research named Technical Operations Research (TOR) has its origin. It combines mathematical and technical know-how for the optimal design of technical systems. We illustrate our optimization approach in a case study for the design of a ventilation system with the ambition to minimize the energy consumption for a temporal distribution of diverse load demands. By combining scaling laws with our optimization methods we find the optimal combination of fans and show the advantage of the use of multiple fans. Y1 - 2015 N1 - Proceedings of FAN 2015, Lyon (France), 15 – 17 April 2015 SP - 1 EP - 11 ER - TY - JOUR A1 - Pöttgen, Philipp A1 - Ederer, Thorsten A1 - Altherr, Lena A1 - Lorenz, Ulf A1 - Pelz, Peter F. T1 - Examination and optimization of a heating circuit for energy-efficient buildings JF - Energy Technology N2 - The conference center darmstadtium in Darmstadt is a prominent example of energy efficient buildings. Its heating system consists of different source and consumer circuits connected by a Zortström reservoir. Our goal was to reduce the energy costs of the system as much as possible. Therefore, we analyzed its supply circuits. The first step towards optimization is a complete examination of the system: 1) Compilation of an object list for the system, 2) collection of the characteristic curves of the components, and 3) measurement of the load profiles of the heat and volume-flow demand. Instead of modifying the system manually and testing the solution by simulation, the second step was the creation of a global optimization program. The objective was to minimize the total energy costs for one year. We compare two different topologies and show opportunities for significant savings. KW - energy transfer KW - heating system KW - programming KW - system optimization KW - technical operations research Y1 - 2015 SN - 2194-4296 U6 - http://dx.doi.org/10.1002/ente.201500252 VL - 4 IS - 1 SP - 136 EP - 144 PB - WILEY-VCH Verlag CY - Weinheim ER - TY - CHAP A1 - Altherr, Lena A1 - Ederer, Thorsten A1 - Farnetane, Lucas S. A1 - Pöttgen, Philipp A1 - Vergé, Angela A1 - Pelz, Peter F. T1 - Multicriterial design of a hydrostatic transmission system via mixed-integer programming T2 - Operations Research Proceedings 2015 N2 - 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. Y1 - 2017 SN - 978-3-319-42901-4 SN - 978-3-319-42902-1 U6 - http://dx.doi.org/10.1007/978-3-319-42902-1_41 N1 - International Conference of the German, Austrian and Swiss Operations Research Societies (GOR, ÖGOR, SVOR/ASRO), University of Vienna, Austria, September 1-4, 2015 SP - 301 EP - 307 PB - Springer CY - Cham ER - TY - JOUR A1 - Altherr, Lena A1 - Ederer, Thorsten A1 - Pöttgen, Philipp A1 - Lorenz, Ulf A1 - Pelz, Peter F. ED - Pelz, Peter F. ED - Groche, Peter T1 - Multicriterial optimization of technical systems considering multiple load and availability scenarios JF - Applied Mechanics and Materials N2 - 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. KW - sustainability KW - availability KW - energy efficiency KW - mixed-integer linear programming KW - system synthesis Y1 - 2015 SN - 1660-9336 U6 - http://dx.doi.org/10.4028/www.scientific.net/AMM.807.247 VL - 807 SP - 247 EP - 256 ER - TY - CHAP A1 - Altherr, Lena A1 - Leise, Philipp T1 - Resilience as a concept for mastering uncertainty T2 - Mastering Uncertainty in Mechanical Engineering Y1 - 2021 SN - 978-3-030-78353-2 U6 - http://dx.doi.org/10.1007/978-3-030-78354-9 N1 - Unterkapitel 6.3.1 des Kapitels "Strategies for Mastering Uncertainty" SP - 412 EP - 417 PB - Springer CY - Cham ER - TY - CHAP A1 - Altherr, Lena A1 - Leise, Philipp A1 - Pfetsch, Marc E. A1 - Schmitt, Andreas T1 - Optimal design of resilient technical systems on the example of water supply systems T2 - Mastering Uncertainty in Mechanical Engineering Y1 - 2021 SN - 978-3-030-78356-3 N1 - Unterkapitel des Kapitels "Strategies for Mastering Uncertainty" SP - 429 EP - 433 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 -