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 - CHAP A1 - Schänzle, Christian A1 - Altherr, Lena A1 - Ederer, Thorsten A1 - Pelz, Peter T1 - TOR – Towards the energetically optimal ventilation system KW - Energy KW - Efficiency KW - Ventilation System KW - Discrete Optimisation KW - TGA Y1 - 2015 N1 - EST 2015, Karlsruhe, 19-21 Mai 2015 ER - TY - CHAP A1 - Stenger, David A1 - Altherr, Lena A1 - Abel, Dirk T1 - Machine learning and metaheuristics for black-box optimization of product families: a case-study investigating solution quality vs. computational overhead T2 - Operations Research Proceedings 2018 N2 - In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead. KW - Product family optimization KW - Mixed-integer nonlinear black-box optimization KW - Engineering optimization KW - Machine learning Y1 - 2019 SN - 978-3-030-18499-5 (Print) SN - 978-3-030-18500-8 (Online) U6 - http://dx.doi.org/10.1007/978-3-030-18500-8_47 SP - 379 EP - 385 PB - Springer CY - Cham ER - TY - CHAP A1 - Stenger, David A1 - Altherr, Lena A1 - Müller, Tankred A1 - Pelz, Peter F. T1 - Product family design optimization using model-based engineering techniques T2 - Operations Research Proceedings 2017 N2 - Highly competitive markets paired with tremendous production volumes demand particularly cost efficient products. The usage of common parts and modules across product families can potentially reduce production costs. Yet, increasing commonality typically results in overdesign of individual products. Multi domain virtual prototyping enables designers to evaluate costs and technical feasibility of different single product designs at reasonable computational effort in early design phases. However, savings by platform commonality are hard to quantify and require detailed knowledge of e.g. the production process and the supply chain. Therefore, we present and evaluate a multi-objective metamodel-based optimization algorithm which enables designers to explore the trade-off between high commonality and cost optimal design of single products. Y1 - 2018 SN - 978-3-319-89919-0 U6 - http://dx.doi.org/10.1007/978-3-319-89920-6_66 SP - 495 EP - 502 PB - Springer CY - Cham ER - TY - JOUR A1 - Sun, Hui A1 - Altherr, Lena A1 - Pei, Ji A1 - Pelz, Peter F. A1 - Yuan, Shouqi T1 - Optimal booster station design and operation under uncertain load JF - Applied Mechanics and Materials N2 - Given industrial applications, the costs for the operation and maintenance of a pump system typically far exceed its purchase price. For finding an optimal pump configuration which minimizes not only investment, but life-cycle costs, methods like Technical Operations Research which is based on Mixed-Integer Programming can be applied. However, during the planning phase, the designer is often faced with uncertain input data, e.g. future load demands can only be estimated. In this work, we deal with this uncertainty by developing a chance-constrained two-stage (CCTS) stochastic program. The design and operation of a booster station working under uncertain load demand are optimized to minimize total cost including purchase price, operation cost incurred by energy consumption and penalty cost resulting from water shortage. We find optimized system layouts using a sample average approximation (SAA) algorithm, and analyze the results for different risk levels of water shortage. By adjusting the risk level, the costs and performance range of the system can be balanced, and thus the system’s resilience can be engineered KW - Stochastic Programming KW - Chance Constraint KW - Engineering Application KW - Pump System KW - Water Distribution Y1 - 2018 U6 - http://dx.doi.org/10.4028/www.scientific.net/AMM.885.102 SN - 1662-7482 VL - 885 SP - 102 EP - 115 PB - Trans Tech Publications CY - Bäch ER - TY - CHAP A1 - Tischbein, Franziska A1 - Kean, Kilian A1 - Vertgewall, Chris Martin A1 - Ulbig, Andreas A1 - Altherr, Lena T1 - Determination of the topology of low-voltage distribution grids using cluster methods T2 - 27th International Conference on Electricity Distribution (CIRED 2023) N2 - Due to the decarbonization of the energy sector, the electric distribution grids are undergoing a major transformation, which is expected to increase the load on the operating resources due to new electrical loads and distributed energy resources. Therefore, grid operators need to gradually move to active grid management in order to ensure safe and reliable grid operation. However, this requires knowledge of key grid variables, such as node voltages, which is why the mass integration of measurement technology (smart meters) is necessary. Another problem is the fact that a large part of the topology of the distribution grids is not sufficiently digitized and models are partly faulty, which means that active grid operation management today has to be carried out largely blindly. It is therefore part of current research to develop methods for determining unknown grid topologies based on measurement data. In this paper, different clustering algorithms are presented and their performance of topology detection of low voltage grids is compared. Furthermore, the influence of measurement uncertainties is investigated in the form of a sensitivity analysis. Y1 - 2023 SN - 978-1-83953-855-1 U6 - http://dx.doi.org/10.1049/icp.2023.0478 N1 - 27th International Conference on Electricity Distribution (CIRED 2023), 12-15 June 2023, Rome, Italy. SP - 1 EP - 5 PB - IEEE ER - TY - JOUR A1 - Vergé, Angela A1 - Pöttgen, Philipp A1 - Altherr, Lena A1 - Ederer, Thorsten A1 - Pelz, Peter F. ED - Greuloch, Ivo ED - Weber, Manfred ED - Meier, Miles T1 - Lebensdauer als Optimierungsziel: Algorithmische Struktursynthese am Beispiel eines hydrostatischen Getriebes JF - O+P – Ölhydraulik und Pneumatik N2 - Verfügbarkeit und Nachhaltigkeit sind wichtige Anforderungen bei der Planung langlebiger technischer Systeme. Meist werden bei Lebensdaueroptimierungen lediglich einzelne Komponenten vordefinierter Systeme untersucht. Ob eine optimale Lebensdauer eine gänzlich andere Systemvariante bedingt, wird nur selten hinterfragt. Technical Operations Research (TOR) erlaubt es, aus Obermengen technischer Systeme automatisiert die lebensdaueroptimale Systemstruktur auszuwählen. Der Artikel zeigt dies am Beispiel eines hydrostatischen Getriebes. Y1 - 2016 SN - 1614-9602 VL - 60 IS - 1-2 SP - 114 EP - 121 PB - Vereinigte Fachverl. CY - Mainz ER -