Journal bearing design using multiobjective genetic algorithm and axiomatic design approaches

This paper describes the optimum design methodology for improving operating characteristics of fluid-film steadily loaded journal bearings. This methodology consists of (1) a simplified closed form solution to accelerate the computation, (2) finite difference mass conserving algorithm for accurate prediction of lubricant flow and power loss, (3) Pareto optimal concept to avoid subjective decision on priority of objective functions, (4) a genetic algorithm to deal with multimodal nature of hydrodynamic-bearing and develop a Pareto optimal front, (5) fitness sharing to maintain genetic diversity of the population used in genetic algorithm, and (6) axiomatic design to provide inside of objective functions and design variables. In the optimum design of journal bearings, the design variables such as radial clearance, length to diameter ratio, groove geometry, oil viscosity and supply pressure are used to simultaneously minimize oil flow and power loss. A step-by-step procedure, graphs and tables are presented to demonstrate the concept and effectiveness of suggested design methodology.