4.7 Article

Nonlinear dynamic analysis of a complex dual rotor-bearing system based on a novel model reduction method

期刊

APPLIED MATHEMATICAL MODELLING
卷 75, 期 -, 页码 553-571

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2019.05.045

关键词

Rotor-bearing system; Rolling element bearing; Model order reduction; CMS method; POD method

资金

  1. National Key Basic Research Program (973 Program) of China [2015CB057400]
  2. National Natural Science Foundation of China [11802235, 11602070]

向作者/读者索取更多资源

In this paper, a dynamic model of a complex dual rotor-bearing system of an aero-engine is established based on the finite element method with three types of beam elements (rigid disc, cylindrical beam element and conical beam element), as well as taking into account the nonlinearities of all of the supporting rolling element bearings. To rapidly and accurately analyze dynamic behaviors of the complex dual rotor-bearing system, a two-level model order reduction (MOR) method is proposed by combining component mode synthesis (CMS) method and proper orthogonal decomposition (POD) technique. The first-level reduced-order model (ROM) of the dual rotors is obtained by CMS method with a high precision for the original system. Then, the POD method is applied to second-level model order reduction to further decrease the degrees of freedom (DOFs) of first-level ROM. Second-level ROM with mode expansion and direct second-level ROM are obtained, and the nonlinear displacement responses of the two ROMs are compared with the first-level ROM. The numerical results demonstrate that the proposed method has a higher computational efficiency and accuracy in terms of mode expansion than the direct model reduction by using POD method. In addition, the nonlinear vibration responses of the dual rotor-bearing system are studied by this second-level ROM in the case of different clearances of the inter-shaft bearing. The results indicate that the dynamic characteristics of the dual rotor-bearing system are very complicated for a large clearance. (C) 2019 Elsevier Inc. All rights reserved.

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