0
Research Papers: Gas Turbines: Structures and Dynamics

Constrained Design Optimization of Rotor-Tilting Pad Bearing Systems

[+] Author and Article Information
Costin D. Untaroiu1

Rotating Machinery and Controls (ROMAC) Laboratories, Department of Mechanical and Aerospace Engineering, University of Virginia, 122 Engineer’s Way, Charlottesville, VA 22904-4746cdu4q@virginia.edu

Alexandrina Untaroiu

Rotating Machinery and Controls (ROMAC) Laboratories, Department of Mechanical and Aerospace Engineering, University of Virginia, 122 Engineer’s Way, Charlottesville, VA 22904-4746au6d@virginia.edu

1

Corresponding author.

J. Eng. Gas Turbines Power 132(12), 122502 (Aug 25, 2010) (7 pages) doi:10.1115/1.4001811 History: Received April 02, 2010; Revised April 26, 2010; Published August 25, 2010; Online August 25, 2010

Design of a rotor-bearing system is a challenging task due to various conflicting design requirements, which should be fulfilled. This study considers an automatic optimization approach for the design of a rotor supported on tilting-pad bearings. A numerical example of a rotor-bearing system is employed to demonstrate the merits of the proposed design approach. The finite element method is used to model the rotor-bearing system, and the dynamic speed-dependent coefficients of the bearing are calculated using a bulk flow code. A number of geometrical characteristics of the rotor simultaneously with the parameters defining the configuration of tilting pad bearings are considered as design variables into the automatic optimization process. The power loss in bearings, stability criteria, and unbalance responses are defined as a set of objective functions and constraints. The complex design optimization problem is solved using heuristic optimization algorithms, such as genetic, and particle-swarm optimization. Whereas both algorithms found better design solutions than the initial design, the genetic algorithms exhibited the fastest convergence. A statistical approach was used to identify the influence of the design variables on the objective function and constraint measures. The bearing clearances, preloads and lengths showed to have the highest influence on the power loss in the chosen design space. The high performance of the best solution obtained in the optimization design suggests that the proposed approach has good potential for improving design of rotor-bearing systems encountered in industrial applications.

FIGURES IN THIS ARTICLE
<>
Copyright © 2010 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

The rotor model of an eight stage centrifugal compressor

Grahic Jump Location
Figure 2

The geometry of tilting pad bearing

Grahic Jump Location
Figure 3

Damped natural frequency constraint for the rotor-bearing system at operating speed (5000 rpm). The unfeasible regions are colored in gray.

Grahic Jump Location
Figure 5

Optimization results: GA-only feasible designs

Grahic Jump Location
Figure 6

Optimization results: PSO algorithm-only feasible designs

Grahic Jump Location
Figure 7

The effect sizes of design variables on the power loss (all p-values under 0.003)

Grahic Jump Location
Figure 8

The maximum unbalance amplitude along the major axis of elliptical orbits at bearing and unbalance mass locations (unbalance cases 1 and 2)

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In