Floating ring bearings are widely used in high-speed turbomachinery such as turbochargers and turbogenerators.Research-ers have recently explored various surface texturing strategies on the inner surface of floating r...Floating ring bearings are widely used in high-speed turbomachinery such as turbochargers and turbogenerators.Research-ers have recently explored various surface texturing strategies on the inner surface of floating rings to enhance bearing performance.In this study,the herring patterns are textured on the inner surface of the floating ring.This pattern is inspired by the secondary flight feathers of the Indian pigeon,which aid the bird in reducing viscous drag during flight.The result-ing Herringbone Textured Floating Ring Bearing(HTFRB)is investigated for its potential application in locomotive turbo-chargers.The HTFRB is numerically modeled using the Reynolds equation to evaluate the bearing's pressure distribution and static characteristics,including load-carrying capacity,power loss,and side leakage.Dynamic characteristics are determined by solving the zeroth-and first-order perturbed Reynolds equation.A Sobol sensitivity analysis is conducted to 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 neural networks and adaptive neuro-fuzzy inference systems,is developed to maximize load carrying capacity while minimiz-ing power loss,side leakage,and friction coefficient.The optimized texture parameters obtained from this framework are employed to validate the ANN model and evaluate the static and dynamic characteristics of the HTFRB.The dynamic coefficients of the HTFRB are further employed to evaluate the stability and robustness of the turbocharger rotor-HTFRB system.This study underscores the potential of combining bio-inspired texture design with numerical modeling and AI-based optimization to develop high-performance HTFRB.展开更多
文摘Floating ring bearings are widely used in high-speed turbomachinery such as turbochargers and turbogenerators.Research-ers have recently explored various surface texturing strategies on the inner surface of floating rings to enhance bearing performance.In this study,the herring patterns are textured on the inner surface of the floating ring.This pattern is inspired by the secondary flight feathers of the Indian pigeon,which aid the bird in reducing viscous drag during flight.The result-ing Herringbone Textured Floating Ring Bearing(HTFRB)is investigated for its potential application in locomotive turbo-chargers.The HTFRB is numerically modeled using the Reynolds equation to evaluate the bearing's pressure distribution and static characteristics,including load-carrying capacity,power loss,and side leakage.Dynamic characteristics are determined by solving the zeroth-and first-order perturbed Reynolds equation.A Sobol sensitivity analysis is conducted to 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 neural networks and adaptive neuro-fuzzy inference systems,is developed to maximize load carrying capacity while minimiz-ing power loss,side leakage,and friction coefficient.The optimized texture parameters obtained from this framework are employed to validate the ANN model and evaluate the static and dynamic characteristics of the HTFRB.The dynamic coefficients of the HTFRB are further employed to evaluate the stability and robustness of the turbocharger rotor-HTFRB system.This study underscores the potential of combining bio-inspired texture design with numerical modeling and AI-based optimization to develop high-performance HTFRB.
