A Parameter Preserving Model Order Reduction Algorithm for Rotordynamic Systems

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 %.