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Design of an artificial neural network predictor for analysis of a hydrodynamic thrust bearing system

Design of an artificial neural network predictor for analysis of a hydrodynamic thrust bearing... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Industrial Lubrication and Tribology Emerald Publishing

Design of an artificial neural network predictor for analysis of a hydrodynamic thrust bearing system

Åžahin Yildirim
Industrial Lubrication and Tribology , Volume 58 (2): 6 – Mar 1, 2006
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References (8)

Publisher
Emerald Publishing
Copyright
Copyright © 2006 Emerald Group Publishing Limited. All rights reserved.
ISSN
0036-8792
DOI
10.1108/00368790610651503
Publisher site
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Abstract

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.

Journal

Industrial Lubrication and Tribology – Emerald Publishing

Published: Mar 1, 2006

Keywords: Lubricants; Neural nets; Elastic properties (fluids)

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