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Approximate Solution of the Fokker-Planck Equation for a Multi-Degree of Freedom Frictionally Damped Bladed Disk Under Random Excitation

[+] Author Affiliations
Alwin Förster, Lars Panning-von Scheidt, Jörg Wallaschek

Leibniz Universität Hannover, Hannover, Germany

Paper No. GT2018-75755, pp. V07AT32A002; 10 pages
doi:10.1115/GT2018-75755
From:
  • ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition
  • Volume 7A: Structures and Dynamics
  • Oslo, Norway, June 11–15, 2018
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-5113-5
  • Copyright © 2018 by ASME

abstract

Bladed Disks are subjected to different types of excitations, which cannot in any case be described in a deterministic manner. Fuzzy factors, such as slightly varying airflow or density fluctuation, can lead to an uncertain excitation in terms of amplitude and frequency, which has to be described by random variables. The computation of frictionally damped blades under random excitation becomes highly complex due to the presence of nonlinearities. Only a few publications are dedicated to this particular problem. Most of these deal with systems of only one or two degrees of freedom and use computational expensive methods, like finite element method (FEM) or finite differences method (FDM), to solve the determining differential equation. The stochastic stationary response of a mechanical system is characterized by the joint probability density function (JPDF), which is driven by the Fokker-Planck equation (FPE). Exact stationary solutions of the FPE only exist for a few classes of mechanical systems. This paper presents the application of a semi-analytical Galerkin-type method to a frictionally damped bladed disk under influence of Gaussian white noise (GWN) excitation in order to calculate its stationary response. One of the main difficulties is the selection of a proper initial approximate solution, which is applicable as a weighting function. Comparing the presented results with those from the FDM, Monte-Carlo Simulation (MCS) as well as analytical solutions proves the applicability of the methodology.

Copyright © 2018 by ASME

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