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Research Papers: Gas Turbines: Structures and Dynamics

An Optimum Design Approach for Textured Thrust Bearing With Elliptical-Shape Dimples Using Computational Fluid Dynamics and Design of Experiments Including Cavitation

[+] Author and Article Information
Gen Fu

Laboratory for Turbomachinery and Components,
Department of Biomedical
Engineering and Mechanics,
Virginia Tech,
Norris Hall, Room 107,
495 Old Turner Street,
Blacksburg, VA 24061
e-mail: gen8@vt.edu

Alexandrina Untaroiu

Laboratory for Turbomachinery and Components,
Department of Biomedical
Engineering and Mechanics,
Virginia Tech,
Norris Hall, Room 324,
495 Old Turner Street,
Blacksburg, VA 24061
e-mail: alexu@vt.edu

Contributed by the Structures and Dynamics Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received December 22, 2016; final manuscript received February 14, 2017; published online April 11, 2017. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(9), 092502 (Apr 11, 2017) (9 pages) Paper No: GTP-16-1594; doi: 10.1115/1.4036188 History: Received December 22, 2016; Revised February 14, 2017

Textured thrust bearings are capable of providing higher load capacity and lower friction torque compared to nontextured bearings. However, most previous optimization efforts for texturing geometry were focused on rectangular dimples and employed Reynolds equation. Limited studies have been done to investigate the effects of partially textured thrust bearings with elliptical dimples. This study proposes a new optimization approach to find the optimal partially texture geometry with elliptical dimples, which maximize the loading capacity and minimize the friction torque. In this study, a 3D computational fluid dynamics (CFD) model for a parallel sector-pad thrust bearing is built using ANSYS cfx. Mass conserving cavitation model is used to simulate the cavitation regions. Energy equation for Newtonian flow is also solved. The results of the model are validated by the experimental data from the literature. Based on this model, the flow pattern and pressure distribution inside the dimples are analyzed. The geometry of elliptical dimple is parameterized and analyzed using design of experiments (DOE). The selected geometry parameters include the length of major and minor axes, dimple depth, radial and circumferential space between two dimples, and the radial and circumferential extend. A multi-objective optimization scheme is used to find the optimal texture structure with the load force and friction torque set as objective functions. The results show that the shape of dimples has a crucial effect on the performance of the textured thrust bearings. Searching the design space for a proper combination among the design variables satisfying the constraints has the advantage of capturing the codependence among design variables and leads to a surface patterning of the bearing, which showed a 42.7% improvement on the load capacity.

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References

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Figures

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Fig. 1

The geometry of the thrust bearing with square dimples

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Fig. 2

Thrust bearing model-configuration with elliptical dimples: (a) lateral view, (b) top view, (c) detailed elliptical dimple geometry, and (d) 3D model of the fluid domain

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Fig. 3

Response surface plots of design variables for load capacity and friction torque: (a) load capacity versus major and minor axis lengths, (b) load capacity versus the distance between dimples in radial and circumferential directions, (c) load capacity versus texture extent in radial and circumferential directions, (d) friction torque versus major and minor axis lengths, (e) friction torque versus the distance between dimples in radial and circumferential directions, and (f) friction torque versus texture extent in radial and circumferential directions

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Fig. 4

Sensitivity of design variables: (a) friction torque and (b) load capacity

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Fig. 5

Goodness-of-fit of the response models for (a) friction torque and (b) load capacity

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Fig. 6

Comparison between model with cavitation and without cavitation

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Fig. 7

Pressure distribution of the baseline model: (a) without cavitation model and (b) with cavitation model

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Fig. 9

The pressure distribution of the optimal design

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Fig. 10

Velocity vectors at different depths (d) of the elliptical dimple for baseline model and optimal model: (a) d = 0.001 mm, (b) d = 0.003 mm, (c) d = 0.006 mm, (d) d = 0.009 mm, (e) d = 0.001 mm, (f) d = 0.008 mm, (g) d = 0.016 mm, and (h) d = 0.027 mm

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