Nonparametric stochastic modeling of uncertainty in rotordynamics-part 1: Formulation

Raghavendra Murthy, Marc Mignolet, Aly El-Shafei

Research output: Contribution to journalArticle

31 Scopus citations

Abstract

A systematic and rational approach is presented for the consideration of uncertainty in rotordynamics systems, i.e., in rotor mass and gyroscopic matrices, stiffness matrix, and bearing coefficients. The approach is based on the nonparametric stochastic modeling technique, which permits the consideration of both data and modeling uncertainty. The former is induced by a lack of exact knowledge of properties such as density, Young's modulus, etc. The latter occurs in the generation of the computational model from the physical structure as some of its features are invariably ignored, e.g., small anisotropies, or approximately represented, e.g., detailed meshing of gears. The nonparametric stochastic modeling approach, which is briefly reviewed first, introduces uncertainty in reduced order models through the randomization of their system matrices (e.g., stiffness, mass, and damping matrices of nonrotating structural dynamic systems). Here, this methodology is extended to permit the consideration of uncertainty in symmetric and asymmetric rotor dynamic systems. More specifically, uncertainties on the rotor stiffness (stiffness matrix) and/or mass properties (mass and gyroscopic matrices) are first introduced that maintain the symmetry of the rotor. The generalization of these concepts to uncertainty in the bearing coefficients is achieved next. Finally, the consideration of uncertainty in asymmetric rotors is described in detail.

Original languageEnglish (US)
Article number092501
JournalJournal of Engineering for Gas Turbines and Power
Volume132
Issue number9
DOIs
StatePublished - Sep 1 2010

Keywords

  • Nonparametric stochastic modeling
  • Random matrices
  • Rotordynamics
  • Uncertain bearings
  • Uncertain rotor
  • Uncertainty

ASJC Scopus subject areas

  • Nuclear Energy and Engineering
  • Fuel Technology
  • Aerospace Engineering
  • Energy Engineering and Power Technology
  • Mechanical Engineering

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