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A.O. Kurban, E. Koç
Design parameters for hydrodynamic thrust bearings and their effect on the system performance
I. Arregui, C. Vázquez (2001)
Finite element solution of a Reynolds–Koiter coupled problem for the elastic journal–bearingComputer Methods in Applied Mechanics and Engineering, 190
Xiaojing Wang, Zhiming Zhang, Guoxian Zhang (1999)
Improving the performance of spring-supported thrust bearing by controlling its deformationsTribology International, 32
C. Sinanoglu, A.O. Kurban
Experimental investigation of elastohydrodynamics lubrication pressure development and capability of load bearing in different ambient temperatures
Ş. Yıldırım (1999)
Neural network for control of bipedsElectronics Letters, 35
E. Koç (1990)
An investigation into the numerical solution of Reynolds' lubrication equation with special reference to thrust bearingsTribology International, 23
D. Reddy, S. Swarnamani, B. Prabhu (2000)
Thermoelastohydrodynamic Analysis of Tilting Pad Journal Bearing—Theory and ExperimentsTribology Transactions, 43
E. Koc, A.O. Kurban
Load capacity on thrust bearings
Purpose – Seeks to present a theoretical analysis on the general behaviour of a thrust bearing. Design/methodology/approach – The model programme using a method adaptation of finite differences was developed to solve the Reynolds equation for lubrication. The model in the theoretical analysis uses a single one‐dimensional grid. The altering of total lubrication load obtained as a result of under‐cutting in the thrust bearing has been determined together with the parameters such as oil film thickness and pressure. Parameters such as the pressure and thickness of the oil film were determined. The hydrodynamic behaviour of thrust bearing was analysed by considering different dimensionless system pressure, speed and geometry of the bearing. The effect of the elastic load due to elastic deflection is taken into account as the load‐bearing characteristics are included. Also, a proposed neural network predictor is utilised to analyse the general behaviour of thrust bearing. Findings – The results of the proposed neural network predictor give superior performance for analysing of the behaviour of a thrust bearing undergoing in elastic deformation. Originality/value – This continuation of theoretical and practical study evaluation should be of benefit to thrust bearing designers and researchers, who hopefully will make significant progress as a result.
Industrial Lubrication and Tribology – Emerald Publishing
Published: Mar 1, 2006
Keywords: Lubricants; Neural nets; Elastic properties (fluids)
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