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.

Numerical study and parameter optimization of partial journal bearing using MOPSO algorithm

“…Later on in another study by them in 2016, 9 a steady-state performance analysis of two-lobe oil journal bearing was also optimized by considering the minimization of friction variable, maximization of non-dimensional load-carrying capacity, and flow rate as objective functions. In 2015, an Artificial Bee Colony algorithm for finding the optimal performance of fixed pad hydrodynamic journal bearings was proposed by Gorasso et al 10 The objective functions were the minimization of the aggregates mass flow, power loss, maximum pressure, temperature rise, and minimum lubricant film thickness. A three-dimensional computational fluid dynamics model on commercial software Ansys Fluent was also employed to calculate the performance of the bearing.…”

Multiobjective optimization of thermohydrodynamic journal bearing using MOPSO algorithm

“…In 2015, they also published a new study 9 about the optimization of a two lobe oil elliptical journal bearing by considering the same objective functions and optimization algorithm. In 2015, Gorasso et al 10 found the optimal performance of fixed pad hydrodynamic journal bearings by minimization of the aggregates mass flow, power loss, maximum pressure, temperature rise, and minimum lubricant film thickness. To calculate the performance of the bearing, they utilized a three-dimensional computational fluid dynamics model using the commercial software Ansys Fluent.…”