Fuzzy inference and multi-objective genetic algorithm based optimization of micro-grooved two-lobe journal bearing operating with magnetorheological lubricant
Restricted accessResearch articleFirst published online 2025
Fuzzy inference and multi-objective genetic algorithm based optimization of micro-grooved two-lobe journal bearing operating with magnetorheological lubricant
This study deals with the finite element method-based simulations of a textured surface, two-lobe journal bearing operating with a magnetorheological (MR) lubricant—a smart fluid composed of fine magnetic particles suspended in a carrier fluid. The flow of the MR lubricant within the textured bearing, which incorporates rectangular micro-grooves along axial directions, is modeled using Reynolds’ equation. The Bingham plastic model defines the MR fluid's viscosity, accounting for the influence of yield stress, magnetic field, and shear-strain rate. The Reynolds equation is solved using the finite element method to determine film pressure, film direct stiffness coefficients, threshold speed, journal trajectories and limit cycles. A Multi-Objective Genetic Algorithm (MOGA) is used to optimize rectangular micro-groove attributes (i.e., number, width, depth and length) to maximize the direct film stiffness coefficients and threshold speed of stability. A robust design recommendation is proposed based on Fuzzy-based Multi-Objective Genetic Algorithm (Fuzzy-MOGA) optimization, targeting improved film stiffness coefficients and threshold speed. The results exhibit a stable and resilient optimization landscape, with minimal sensitivity to input variations. The numerical results demonstrate that the combined use of MR lubricant, two-lobe geometry and surface texturing enhances stiffness coefficients (116.7%∼850.8%) and threshold speed (81.3%). The two-lobe bearing with partial surface texturing in the leading half demonstrates the highest dynamic stability, as evidenced by threshold speed, minimal journal center trajectories and compact limit cycles.
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