Journal Bearing Optimization Using Nonsorted Genetic Algorithm and Artificial Bee Colony Algorithm

Abstract

In this work, a journal bearing optimization process has been developed and is divided into two stages. Each one has a set of decision variables and custom objectives aggregating performances with a weighting strategy. The performance functions used are an artificial neural network, trained with Reynolds equation solutions, and a CFD simulation of the bearings carried out with commercial software. The results show the capabilities of the algorithm to design and optimize journal bearings by reducing both power loss and mass flow with respect to ones designed with traditional methods, as well as by minimizing the maximum and average temperature.