MOGA-Based Optimization and Performance Comparison of Plain and Multi-Lobe Hydrodynamic Journal Bearings

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. The three-lobe bearing proved to be the most suitable choice based on its superior load-carrying capacity, minimal temperature rise, reduced friction loss, and overall stability performance. The findings reveal that the four-lobe bearing excels in LCC, while the plain and two-lobe bearings are advantageous for their simple design and low manufacturing costs. These results offer valuable insights for engineers and designers in choosing the most appropriate bearing type based on specific operational needs and performance trade-offs.