Geometrical optimization of hydrodynamic journal bearings with validated simulations and artificial intelligence tools

Abstract

The scope of this work is to mathematically optimize the choice of geometrical parameters of a hydrodynamic journal bearing to maximize its performance. Despite the fact that several works have investigated methods to predict the optimal shape of this family of sliding bearings, significant opportunities remain to improve the efficiency of the algorithm through the use of validated computational fluid dynamics and intelligent stochastic algorithms to find the function’s maximum and minimum. This work presents a set of experiments carried out to validate simulations of fluid film bearings. These virtual models are used to determine the temperatures inside the bearing film. A series of objective functions were built and minimized in order to maximize the performance of the bearing and obtain the optimal combination of geometrical parameters for the design. The paper shows the capabilities of the algorithm to improve an existing journal bearing design as well as the validation data of the CFD simulations.