Modelling pressure distribution in a rectangular gas bearing using neural networks Academic Article

abstract

• Gas lubricated bearings are of tremendous use especially in the biomedical and aerospace industries. For that reason, gas bearings have been the subject of much research for the past decade or so. Experimental as well as theoretical work has been done to calculate the pressure distribution inside the bearing. The models available to predict the pressure are primitive and need to be improved. This paper discusses a new modelling scheme known as artificial neural networks. The pressure distribution and the load-carrying capacity are predicted using feedforward architecture of neurons. The inputs to the networks are a collection of experimental data. This data is used to train the network using the Levenberg-Marquardt optimization technique. The results of the neural network model are compared to a theoretical model and the results are promising. The neural network model outperforms the available theoretical model in predicting the pressure as well as the load-carrying capacity. Copyright 1996 Elsevier Science Ltd.

authors

• Karkoub, Mansour

published proceedings

• TRIBOLOGY INTERNATIONAL

author list (cited authors)

• Karkoub, M., & Elkamel, A.

citation count

• 21

complete list of authors

• Karkoub, M||Elkamel, A

publication date

• February 1997

publisher

• Elsevier  Publisher

published in

• Tribology International  Journal

keywords

• Backpropagation
• Gas Bearings
• Levenberg-marquardt Technique
• Neural Networks

Digital Object Identifier (DOI)

• 10.1016/0301-679X(96)00038-2

start page

• 139

end page

• 150

volume

• 30

issue

• 2