Investigation of Static and Dynamic Performance of Gas Lubricated Bearing Using Artificial Neural Networks

Document Type : Original Article

Authors

mohamad reza Dehghanizade Baghdadabadi

asghar Rahmatabadi

S. mohamad taghi Almodarresi

10.22067/fum-mech.v22i1.9022

Abstract

Gas lubricated bearings are of tremendous use especially in the biomedical and aerospace industries. Analytical treatment of gas lubrication is tedious due to high nonlinearity of the pressure equation as the consequence of lubricant compressibility. Howevere, in this paper a feed-forward neural network approach is employed to investigate the performance of circular as well as two, three and four-lobe noncircular gas lubricated bearings. The performance parameters considered are stability margins, power loss, bearing load capacity and attitude angle for various values of bearing aspect ratio, eccentricity and compressibility numbers. The results of the neural network analyses are compared with those obtained from the finite element model. It is observed that results are in good agreement. It is believed that the neural network model can easily compete with the available theoretical model in predicting the solution of lubrication problems in respect to its simplicity, as well as its capability of producting accurate results with lesser computer time.

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