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TECHNICAL PAPERS: Gas Turbines: Structures and Dynamics

Constrained Optimization of Gas Turbine Tilting Pad Bearing Designs

[+] Author and Article Information
Anders Angantyr

 ALSTOM Power Sweden AB, 72176 Västerås, Swedenanders.angantyr@power.alstom.com

Jan-Olov Aidanpää

Division of Computer Aided Design, Department of Applied Physics and Mechanical Engineering, Luleå University of Technology, 97187 Luleå, Swedenjoa@cad.luth.se

J. Eng. Gas Turbines Power 128(4), 873-878 (Oct 25, 2005) (6 pages) doi:10.1115/1.2179463 History: Received May 11, 2004; Revised October 25, 2005

This paper presents the constrained optimization of the tilting pad bearing design on a gas turbine rotor system. A real coded genetic algorithm with a robust constraint handling technique is used as the optimization method. The objective is to develop a formulation of the optimization problem for the late bearing design of a complex rotor-bearing system. Furthermore, the usefulness of the search method is evaluated on a difficult problem. The effects considered are power loss and limiting temperatures in the bearings as well as the dynamics at the system level, i.e., stability and unbalance responses. The design variables are the bearing widths and radial clearances. A nominal design is the basis for comparison of the optimal solution found. An initial numerical experiment shows that finding a solution that fulfills all the constraints for the system design is likely impossible. Still, the optimization shows the possibility of finding a solution resulting in a reduced power loss while not violating any of the constraints more than the nominal design. Furthermore, the result also shows that the used search method and constraint handling technique works on this difficult problem.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

Schematic sketch of rotor

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Figure 2

Tilting pad bearing geometry

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Figure 4

Bearing temperature for randomly generated designs (dots), nominal design (star), and optimal design (square)

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Figure 5

Power loss for randomly generated designs (dots), nominal design (star), and optimal design (square)

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Figure 6

Search history in optimization

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