Modellbasierte Ansteuerung räumlich ausgedehnter Aktuator- und Sensornetzwerke in der Strömungsregelung

  • Model-based driving of spatially distributed actuator and sensor networks in flow control

Dück, Marcel; Abel, Dirk (Thesis advisor); Schröder, Wolfgang (Thesis advisor)

Jülich : Forschungszentrum Jülich GmbH, Zentralbibliothek (2016, 2017)
Book, Dissertation / PhD Thesis

In: Schriften des Forschungszentrums Jülich. Reihe Energie & Umwelt 349
Page(s)/Article-Nr.: XIII, 153 Seiten : Illustrationen, Diagramme

Dissertation, RWTH Aachen University, 2016


The topic of this thesis deals with the model-based development of a realtime, spatially enlarged actuator and sensor network for use in flow control. Within a cascaded control loop, the external flow control is connected via the network with defined interfaces as model-in-the-loop to the electromagnetic actuator system for influencing the flow. The flow is influenced by means of transversal surface waves on a three millimeter thick aluminum plate.This approach allows both experiments in the wind tunnel as well as the analysis of differences in network configurations, which lead to the determination of a favorable topology and computation distribution. This forms the basis for the specification of network configurations for the technical implementation of a spatially enlarged actuator and sensor network.The necessary tasks are mapped to the corresponding network nodes using a model. The communication layers are defined according to the OSI reference model. A real-time protocol is integrated on the transport layer and verified by a simulation. Various network simulations are investigated with regard to different boundary conditions and configurations and the results are discussed. A method for real-time calculation and application of smooth signal transitions between differently parameterized sinusoidal signals for driving the electromagnetic actuator system is presented. Using the wave control, the system is stabilized and the accuracy of the wave motion is ensured. For this purpose, an adapted model-based iterative learning control with gain switching is developed.