![]() ![]() Applying the three trained Neural Networks (Feed forward, Radial basis, and Generalized regression) to predict the performance of tilting pad bearing at values not used in the training process. ![]() The second is a radial bases Neural Network and the third is a generalized regression Neural Network. The first is a feed-forward Neural Network which consists of three layers with neurons. The characteristic data obtained from COMSOL program is used to train three suggested neural networks. Decreasing the clearance between pads leads to an increase in the load carrying capacity of the bearing while it has minor effect on the other performance parameters. It was found that as the number of pads increases the load carrying capacity and the friction coefficient increase, while the attitude angle decreases. The effect of changing pad numbers (from 3 to 6 pads) and the pads clearance angles (from 2° to 6°) on the performance parameters such as the load carrying capacity, frictional torque and attitude angle was analyzed. COMSOL Multiphysics software is used to simulate the tilting pad journal bearing at different eccentricity ratios and pad clearances. ![]() The objective of this paper is to study the effect of geometrical parameters on the performance of tilting pad journal bearing. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |