Different geometry patterns of the surface of thrust bearings have been proven very beneficial in terms of bearing load-carrying capacity and friction coefficient. In this study, four different types of sector-pad thrust bearings have been cross-evaluated for operation under realistic operating conditions: (a) an open pocket bearing, (b) a closed pocket bearing, (c) a tapered-land bearing, and (d) a bearing partially textured with rectangular dimples. Bearing performance has been computed by means of computational fluid dynamics simulations based on the numerical solution of the Navier–Stokes and energy equations for incompressible flow. Conjugate heat transfer at the bearing pad and rotor has been taken into account. Initially, for a reference design of each bearing, the effects of varying rotational speed and minimum film thickness have been investigated. Further, characterization of each bearing for a constant level of thrust load has been performed. Finally, the effects of varying the depth of each geometry pattern have been studied. The present results illustrate a superior performance of the open pocket bearing in comparison to the other bearing types.

An optimization study of trapezoidal surface texturing in slider micro-bearings, via computational fluid dynamics (CFD), is presented. The bearings are modeled as micro-channels, consisting of a moving and a stationary wall. The moving wall (rotor) is assumed smooth, while part of the stationary wall (stator) exhibits periodic dimples of trapezoidal form. The extent of the textured part of the stator and the dimple geometry are defined parametrically; thus, a wide range of texturing configurations is considered. Flow simulations are based on the numerical solution of the Navier–Stokes equations for incompressible isothermal flow. To optimize the bearing performance, an optimization problem is formulated and solved by coupling the CFD code with an optimization tool based on genetic algorithms and local search methods. Here, the design variables define the bearing geometry, while load carrying capacity is the objective function to be maximized. Optimized texturing geometries are obtained for the case of parallel bearings for several numbers of dimples, illustrating significant load carrying capacity levels. Further, these optimized texturing patterns are applied to converging bearings for different convergence ratio values; the results demonstrate that, for small and moderate convergence ratios, a substantial increase in load carrying capacity, in comparison to smooth bearings, is obtained. Finally, an optimization study performed at a high convergence ratio shows that, in comparison to the parallel slider, the optimal texturing geometry is substantially different, and that performance improvement over smooth bearings is possible even for steep sliders.

Journal and thrust bearings utilise hydrodynamic lubrication to reduce friction and wear between the shaft and the bearing. The process to determine the lubricant film thickness or the actual applied load is vital to ensure proper and trouble-free operation. However, taking accurate measurements of the oil film thickness or load in bearings of operating engines is very difficult and requires specialised equipment and extensive experience. In the present work, the performance parameters of journal bearings of the same principal dimensions are measured experimentally, aiming at training a Machine Learning (ML) algorithm capable of predicting the loading condition of any similar bearing. To this end, an experimental procedure using the Bently Nevada Rotor Kit 4 is set up, combined with sound and vibration measurements in the vicinity of the journal bearing structure. First, sound and acceleration measurements for different values of bearing load and rotational speed are collected and post-processed utilising 1/3 octave band analysis techniques, for parametrisation of the input datasets of the ML algorithms. Next, several ML algorithms are trained and tested. Comparison of the results produced by each algorithm determines the fittest one for each application. The results of this work demonstrate that, in a laboratory environment, the operational parameters of journal bearings can be efficiently identified utilising non-intrusive sound and vibration measurements. The presented approach may substantially improve bearing condition identification and monitoring, which is an imperative step to prevent journal bearing failures and conduct condition-based maintenance.

In the present paper, a study of stiffness and damping in sector-pad micro thrust bearings with artificial surface texturing is presented, based on computational fluid dynamics (CFD) simulations. The bearing pads are modeled as consecutive three-dimensional independent microchannels, each consisting of a smooth rotating wall (rotor) and a partially textured stationary wall (stator). CFD simulations are performed, consisting in the numerical solution of the Navier–Stokes equations for incompressible isothermal flow. The goal of the present study is to characterize the dynamic behavior of favorable designs, identified in previous optimization studies, comprising parallel and convergent thrust bearings with rectangular texture patterns. To this end, a translational degree of freedom (DOF) along the thrust direction and a rotational (tilting) DOF of the rotor are considered. By implementing appropriate small perturbations around the equilibrium (steady-state) position and processing the simulation results, the stiffness and damping coefficients of the bearing are obtained for each DOF. The computed dynamic coefficients of textured thrust bearings are compared to those of conventional (smooth slider) designs. It is found that the dependence of bearing stiffness and damping on geometrical parameters exhibits the same trends for both DOFs. Both stiffness and damping are found to increase with bearing width. In general, increasing the bearing convergence ratio results in increased bearing stiffness and decreased damping. Finally, the present results demonstrate that properly textured parallel sliders are characterized by an overall dynamic performance that is superior to that of smooth converging sliders.

In this paper, a computational investigation of thermohydrodynamic performance and mechanical deformations of a fixed-geometry thrust bearing with artificial surface texturing is presented. A parallel eight-pad bearing is considered; the surface of each pad is partially textured with square dimples. Here, a CFD-based thermohydrodynamic modeling approach, recently introduced by the authors, is used to calculate the performance of the bearing; the THD results are then used to quantify the deformations of the bearing mechanical parts. The bearing is modelled as a sector-shaped channel, consisting of a smooth rotating wall (thrust collar) and a partially textured stationary wall (bearing pad). The bearing performance characteristics are computed by means of numerical simulations, based on the numerical solution of the Navier-Stokes and energy equations for incompressible flow, as well as on the solution of the elasticity equations for the bearing solid parts. Here, a reference texture geometry is considered, while proper thermal and structural boundary conditions are implemented. For representative film thickness values, the effect of rotational speed and collar thickness on bearing performance is quantified, and the resulting pad and rotor deformation fields are computed. It is found that, due to oil heating, the load carrying capacity decreases with rotational speed for values higher that approximately 2000 rpm. The computed rotor deformation field is representative of a fixed support beam, characterized by substantially higher levels than those of the bearing pad. Rotor deformations increase substantially at low values of collar thickness.