Optimal Bearing Configuration Selection for Power Generation Shaft-Trains: A Linear and Nonlinear Dynamics Approach
Document Type
Article
Publication Date
3-17-2025
Publication Title
Journal of Sound and Vibration
Abstract
This paper proposes a straightforward procedure for defining bearing design configurations in turbine-generator shaft trains. The bearing design inputs specify the bearing type and the pad configuration. The design outputs focus on stability, bearing integrity, and operability of the shaft-train system. The design output is evaluated in two ways: a) linear harmonic analysis utilizing linearized stiffness and damping coefficients for the bearing impedance forces, and b) nonlinear analysis, where the bearing forces are modeled as nonlinear functions of bearing and pedestal kinematics; the response is evaluated by collocation-type method coupled with numerical continuation. Thermohydrodynamic lubrication (THD lubrication) with turbulence correction is considered in the bearing lubrication model. The results show that all constraints are satisfied, and the optimal bearing configurations include preload and offset, while no specific trend is observed for specific loads. Laminar oil flow is prompted by the optimization through specific bearing diameters. Linear and nonlinear dynamic models do not render identical optimal designs. Linear model tends to be conservative in the design output, while nonlinear dynamic model provides more accurate predictions, accounting for any whirling orbit shape. The results emphasize the necessity of incorporating nonlinear dynamics into standard rotor dynamic calculations for this type of machines.
Recommended Citation
Chasalevris, Athanasios; Gavalas, Ioannis; and Sawicki, Jerzy T., "Optimal Bearing Configuration Selection for Power Generation Shaft-Trains: A Linear and Nonlinear Dynamics Approach" (2025). Mechanical Engineering Faculty Publications. 445.
https://engagedscholarship.csuohio.edu/enme_facpub/445
DOI
10.1016/j.jsv.2024.118907
Volume
599
Comments
The work was partially funded by the Hellenic Foundation for Research and Innovation (HFRI) under the 4th Call for HFRI PhD Fellowships (Fellowship Number: 9575) .