Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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Simultaneous identification of wind turbine vibrations by using seismic data, elastic modeling and laser Doppler vibrometry

2020 , Zieger, Toni , Nagel, S. , Lutzmann, Peter , Kaufmann, Ilja , Ritter, J. , Ummenhofer, T. , Knödel, P. , Fischer, Peter

This work compares continuous seismic ground motion recordings over several months on top of the foundation and in the near field of a wind turbine (WT) at Pfinztal, Germany, with numerical tower vibration simulations and simultaneous optical measurements. We are able to distinguish between the excitation of eigenfrequencies of the tower-nacelle system and the influence of the blade rotation on seismic data by analyzing different wind and turbine conditions. We can allocate most of the major spectral peaks to either different bending modes of the tower, flapwise, and edgewise bending modes of the blades or multiples of the blade-passing frequency after comparing seismic recordings with tower simulation models. These simulations of dynamic properties of the tower are based on linear modal analysis performed with finite beam elements. To validate our interpretations of the comparison of seismic recordings and simulations, we use optical measurements of a laser Doppler vibrometer at the tower of the turbine at a height of about 20 m. The calculated power spectrum of the tower vibrations confirms our interpretation of the seismic peaks regarding the tower bending modes. This work gives a new understanding of the source mechanisms of WT-induced ground motions and their influence on seismic data by using an interdisciplinary approach. Thus, our results may be used for structural health purposes as well as the development of structural damping methods, which can also reduce ground motion emissions from WTs. Furthermore, it demonstrates how numerical simulations of wind turbines can be validated by using seismic recordings and laser Doppler vibrometry.

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Application and evaluation of meta-model assisted optimisation strategies for gripper assisted fabric draping in composite manufacturing

2018 , Zimmerling, Clemens , Pfrommer, Julius , Liu, Jinzhao , Beyerer, Jürgen , Henning, Frank , Kärger, Luise

With respect to their extraordinary weight-specific mechanical properties, continuous Fibre Reinforced Plastics (CoFRP) have drawn increasing attention for use in load bearing structures. Contrasting metals, manufacturing of CoFRPs components requires multiple steps, often including a draping process of textiles. To predict and optimise the manufacturing process, Finite-Element (FE) simulation methods are being developed along virtual process chains. For maximum part quality, draping process parameters need to be optimised, which requires numerous computationally expensive iterations. While efforts have been made for time-efficient process optimisation in metal forming, composite draping optimisation has is a comparably young discipline and still lacks time-efficient optimisation strategies. In this work, modelling strategies for time-efficient optimisation using computationally inexpensive meta-models are examined, which are used to guide the search for optima in the parameter space. The meta-models are trained by observations of FE-based draping simulations of an automotive part, thereby learning the relationship between variable gripper forces (input) and the resulting shear angles (output). Parametric model functions are compared against deep neural networks (DNN) as non-parametric models with respect to prediction accuracy. Best results are achieved using a DNN that predicts the shear angles of more than 24 000 fabric shell elements.

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