Abstract
To simulate the behavior of large rotors it is necessary to model them using adequate FE-models. The simulation time for such models could be quite long. The models of rotors are challenging for model order reduction (MOR) techniques. The models depend on rotational speed, because the gyroscopic effect is not negligible. Therefore a new parameter preserving model order reduction algorithm—called Ω preserving MOR (ΩP-MOR)—is presented in this work. If the ΩP-MOR algorithm is used with the Krylov subspace method, an arbitrary number of moments could be matched for every value of the parameter. With this work, moment matching is proven for the first time. To demonstrate the efficiency of the algorithm, the reduction and simulation of a real rotor model is shown. With the ΩP-MOR algorithm more than 99.9 % of simulation time could be saved with an error factor smaller than 0.1 %.
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References
Antoulas A C (2005) Approx. of Large-Scale Dynamical Sys. SIAM, Philadelphia
Krenek K (2012) Modell Ordnungsreduktion grosser Systeme unter rotordynamischem Einfluss. Dr. Hut, Muenchen. ISBN 978-3-8439-0642-5
Daniel L, Siong OC, Chay LS, Lee KH, White J (2004) A multiparameter moment-matching model-reduction approach for generating geometrically parameterized interconnect performance models. IEEE Trans Comput Aided Des Integr Circ Syst 23:678–693
Yamamoto T, Ishida Y (2001) Linear and nonlinear rotordynamics: a modern treatment with applications. Wiley-Interscience
Friswell MI, Penny JET, Garvey SD, Lees AW (2010) Dynamics of rotating machines. Cambridge University Press, Cambridge
Freund RW (2003) Model reduction methods based on Krylov subspaces. Acta Numerica 12:267–319
Grimme EJ (1997) Krylov projection methods for model reduction. PhD thesis, Department of Electrical Engineering, Uni. Illinois at Urbana Champaign
Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1992) Numerical recipes in FORTRAN 77: the art of scientific computing. Cambridge University Press, Cambridge. ISBN: 978-0-521-43064-7
Matlab and Simulink version R2012a, MathWorks www.mathworks.com/products/
Krenek K, Eid R, Lohmann B (2010) Model order reduction of rotordynamical systems. IFAC, Milano
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Krenek, K. (2015). A Parameter Preserving Model Order Reduction Algorithm for Rotordynamic Systems. In: Pennacchi, P. (eds) Proceedings of the 9th IFToMM International Conference on Rotor Dynamics. Mechanisms and Machine Science, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-319-06590-8_154
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DOI: https://doi.org/10.1007/978-3-319-06590-8_154
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