Texture Optimization in Hydrodynamic Bearings Using Evolutionary Strategies

Abstract

Surface texturing in a hydrodynamic bearing consists of introducing well-defined and intentional cavities on one of its surfaces. This technique has generated growing interest in recent literature, where an increase in load-carrying capacity has been demonstrated, although no clear consensus exists regarding design criteria and optimal placement. In this work, an automatic optimization of textures in hydrodynamic bearings is presented, as a continuation of a previous study in which the incorporation of textures was explored manually. The study variables considered at this stage correspond to the position and depth of the cavities, aiming to maximize load capacity and minimize friction. The optimizer was developed in Python using evolutionary strategies, and the bearing fluid dynamics are solved through a model formulated with the Finite Area Method and implemented in the OpenFOAM(R) platform.