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

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.