MOGA-Based Optimization and Performance Comparison of Plain and Multi-Lobe Hydrodynamic Journal Bearings - Ahire, Nitin B.; Deshmukh, Dhiraj D. (Zielona Góra: Uniwersytet Zielonogórski)
Choosing the best hydrodynamic journal bearing (HJB) involves a complex multi-objective optimization challenge that requires balancing load-carrying capacity (LCC), friction loss, oil film temperature increase, and dynamic stability. This research utilizes the multi-objective genetic algorithm (MOGA) to optimize plain, two-lobe, three-lobe, and four-lobe journal bearings under different operating conditions. The variable parameters of HJBs, including rotational speed, clearance, L/D ratio, and load, were taken into account. The optimization process utilized Pareto-based selection, simulated binary crossover, and Gaussian mutation techniques to determine the optimal bearing choice.
Keywords: HJBs; multi objective optimization; load carrying capacity; friction loss; stability