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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%.
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.
Nahezu 100.000 denkbare Strukturen kann ein Getriebe bei gleicher Funktion aufweisen - je nach Ganganzahl und gefordertem Freiheitsgrad. Mit dem traditionellen Ansatz bei der Entwicklung, einzelne vielversprechende Systemkonfigurationen manuell zu identifizieren und zu vergleichen, können leicht innovative und vor allem kostenminimale Lösungen übersehen werden. Im Rahmen eines Forschungsprojekts hat die TU Darmstadt spezielle Optimierungsmethoden angewendet, um auch bei großen Lösungsräumen zielsicher ein für die individuellen Zielstellungen optimales Layout zu finden.
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.
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.
On obligations in the development process of resilient systems with algorithmic design methods
(2018)
Advanced computational methods are needed both for the design of large systems and to compute high accuracy solutions. Such methods are efficient in computation, but the validation of results is very complex, and highly skilled auditors are needed to verify them. We investigate legal questions concerning obligations in the development phase, especially for technical systems developed using advanced methods. In particular, we consider methods of resilient and robust optimization. With these techniques, high performance solutions can be found, despite a high variety of input parameters. However, given the novelty of these methods, it is uncertain whether legal obligations are being met. The aim of this paper is to discuss if and how the choice of a specific computational method affects the developer’s product liability. The review of legal obligations in this paper is based on German law and focuses on the requirements that must be met during the design and development process.
Algorithmic design and resilience assessment of energy efficient high-rise water supply systems
(2018)
High-rise water supply systems provide water flow and suitable pressure in all levels of tall buildings. To design such state-of-the-art systems, the consideration of energy efficiency and the anticipation of component failures are mandatory. In this paper, we use Mixed-Integer Nonlinear Programming to compute an optimal placement of pipes and pumps, as well as an optimal control strategy.Moreover, we consider the resilience of the system to pump failures. A resilient system is able to fulfill a predefined minimum functionality even though components fail or are restricted in their normal usage. We present models to measure and optimize the resilience. To demonstrate our approach, we design and analyze an optimal resilient decentralized water supply system inspired by a real-life hotel building.