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Author

  • Lena Altherr (7)
  • Peter F. Pelz (7)
  • Ulf Lorenz (7)
  • Thorsten Ederer (6)
  • Philipp Pöttgen (4)
  • Christian Schänzle (2)
  • Alexander Matei (1)
  • Andreas Schmitt (1)
  • Anja Kuttich-Meinlschmidt (1)
  • Christian Bölling (1)
  • Dirk A. Molitor (1)
  • Eberhard Abele (1)
  • Eckhard Kirchner (1)
  • Felix Geßner (1)
  • Fiona Schulte (1)
  • Florian Hoppe (1)
  • Hermann Kloberdanz (1)
  • Ingo Dietrich (1)
  • Johann M. Schmitt (1)
  • Manuel Rexer (1)
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Year of publication

  • 2021 (1)
  • 2017 (1)
  • 2016 (1)
  • 2015 (3)
  • 2014 (1)

Document Type

  • Article (3)
  • Part of a Book (2)
  • Conference Proceeding (2)

Keywords

  • Optimal Topology (2)
  • Controller Parameter (1)
  • Level Control System (1)
  • Optimal Closed Loop (1)
  • Piecewise Linearization (1)
  • Similarity Theory (1)
  • Technical Operation Research (1)
  • Ventilation System (1)
  • availability (1)
  • energy efficiency (1)
  • energy transfer (1)
  • heating system (1)
  • mixed-integer linear programming (1)
  • programming (1)
  • sustainability (1)
  • system optimization (1)
  • system synthesis (1)
  • technical operations research (1)

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Examination and optimization of a heating circuit for energy-efficient buildings (2015)
Philipp Pöttgen ; Thorsten Ederer ; Lena Altherr ; Ulf Lorenz ; Peter F. Pelz
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.
Designing a feedback control system via mixed-integer programming (2016)
Lena Altherr ; Thorsten Ederer ; Ulf Lorenz ; Peter F. Pelz ; Philipp Pöttgen
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.
Strategies for mastering uncertainty (2021)
Marc E. Pfetsch ; Eberhard Abele ; Lena Altherr ; Christian Bölling ; Nicolas Brötz ; Ingo Dietrich ; Tristan Gally ; Felix Geßner ; Peter Groche ; Florian Hoppe ; Eckhard Kirchner ; Hermann Kloberdanz ; Maximilian Knoll ; Philip Kolvenbach ; Anja Kuttich-Meinlschmidt ; Philipp Leise ; Ulf Lorenz ; Alexander Matei ; Dirk A. Molitor ; Pia Niessen ; Peter F. Pelz ; Manuel Rexer ; Andreas Schmitt ; Johann M. Schmitt ; Fiona Schulte ; Stefan Ulbrich ; Matthias Weigold
This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems.
Experimental validation of an enhanced system synthesis approach (2014)
Lena Altherr ; Thorsten Ederer ; Ulf Lorenz ; Peter F. Pelz ; Philipp Pöttgen
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%.
Algorithmic system design using scaling and affinity laws (2017)
Lena Altherr ; Thorsten Ederer ; Christian Schänzle ; Ulf Lorenz ; Peter F. Pelz
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.
As good as it can be: Ventilation system design by a combined scaling and discrete optimization method (2015)
Christian Schänzle ; Lena Altherr ; Thorsten Ederer ; Ulf Lorenz ; Peter F. Pelz
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.
Multicriterial optimization of technical systems considering multiple load and availability scenarios (2015)
Lena Altherr ; Thorsten Ederer ; Philipp Pöttgen ; Ulf Lorenz ; Peter F. Pelz
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.
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