A methodology for performance prediction: Hydrodynamic investigation of spiral grooved thrust bearing

This paper presents the design and numerical optimization of oil-lubricated spiral grooved thrust bearing (SGTB) for its application at high speed and axial loading conditions. A numerical model is developed using nonlinear incompressible Reynold's equation and is solved using the finite volume method (FVM) to determine the static characteristics over the bearing surface. Further, the influence of groove parameters such as spiral angle, groove angle, film thickness ratio, number of grooves and speed on the static characteristics of the bearing has been investigated. The result shows that the designed oil-lubricated SGTB can operate at high-speed conditions and withstand high axial load. Further, the characteristic data sets acquired from the numerical analysis are trained using an artificial neural network (ANN), and their performance is evaluated through the computation of the regression coefficient. Then adaptive neuro-fuzzy interface system (ANFIS) surface plot is obtained to determine the optimum bearing parameters.