Abstract: Floating ring bearings are widely used in high-speed turbomachinery such as turbochargers and turbogenerators. Researchers have recently explored various surface texturing strategies on the inner surface of floating rings to enhance bearingperformance. In this study, the herring patterns are textured on the inner surface of the floating ring. This pattern is inspiredby the secondary flight feathers of the Indian pigeon, which aid the bird in reducing viscous drag during flight. The resulting Herringbone Textured Floating Ring Bearing (HTFRB) is investigated for its potential application in locomotive turbochargers. The HTFRB is numerically modeled using the Reynolds equation to evaluate the bearing’s pressure distributionand static characteristics, including load-carrying capacity, power loss, and side leakage. Dynamic characteristics aredetermined by solving the zeroth- and first-order perturbed Reynolds equation. A Sobol sensitivity analysis is conductedto quantify the influence of groove parameters — helix angle, groove depth, groove width ratio, and number of grooves— on bearing performance metrics. An artificial intelligence-based optimization framework, integrating artificial neuralnetworks and adaptive neuro-fuzzy inference systems, is developed to maximize load carrying capacity while minimizing power loss, side leakage, and friction coefficient. The optimized texture parameters obtained from this framework areemployed to validate the ANN model and evaluate the static and dynamic characteristics of the HTFRB. The dynamiccoefficients of the HTFRB are further employed to evaluate the stability and robustness of the turbocharger rotor-HTFRBsystem. This study underscores the potential of combining bio-inspired texture design with numerical modeling and AIbased optimization to develop high-performance HTFRB.

Key words: Floating ring bearing, Surface texture, Herringbone pattern, Hydrodynamic lubrication, Dynamiccoefficient, Stability analysis, Turbocharger, Sobol sensitivity
')">Floating ring bearing, Surface texture, Herringbone pattern, Hydrodynamic lubrication, Dynamiccoefficient, Stability analysis, Turbocharger, Sobol sensitivity