An Experimental Test Set-Up Design for Acquiring Creep Curve of the Spacer Between the Winding Turns of Power Transformers

Author(s):  
Ahmet Yigit ARABUL ◽  
Fatma KESKIN ARABUL ◽  
Celal Fadil KUMRU ◽  
Ibrahim SENOL
Author(s):  
S Van Dam ◽  
J Pelfrene ◽  
W Van Paepegem ◽  
J Degrieck ◽  
D Lecompte ◽  
...  

Author(s):  
K. Geebelen ◽  
H. Ahmad ◽  
M. Vukov ◽  
S. Gros ◽  
J. Swevers ◽  
...  

2006 ◽  
Vol 29 (3) ◽  
pp. 14195 ◽  
Author(s):  
L David Suits ◽  
TC Sheahan ◽  
J Black ◽  
V Sivakumar ◽  
MR Madhav ◽  
...  
Keyword(s):  
Test Set ◽  

Author(s):  
Y K Ahn ◽  
J-Y Ha ◽  
Y-H Kim ◽  
B-S Yang ◽  
M Ahmadian ◽  
...  

This paper presents an analytical and experimental analysis of the characteristics of a squeeze-type magnetorheological (MR) mount which can be used for various vibration isolation areas. The concept of the squeeze-type mount and details of the design of a squeeze-type MR mount are discussed. These are followed by a detailed description of the test set-up for evaluating the dynamic behaviour of the mount. A series of tests was conducted on the prototype mount built for this study, in order to characterize the changes occurring as a result of changing electrical current to the mount. The results of this study show that increasing electrical current to the mount, which increases the yield stress of the MR fluid, will result in an increase in both stiffness and damping of the mount. The results also show that the mount hysteresis increases with increase in current to the MR fluid, causing changes in stiffness and damping at different input frequencies.


2010 ◽  
Vol 171-172 ◽  
pp. 274-277
Author(s):  
Yun Liang Tan ◽  
Ze Zhang

In order to quest an effective approach for predicate the rheologic deformation of sandstone based on some experimental data, an improved approaching model of RBF neural network was set up. The results show, the training time of improved RBF neural network is only about 10 percent of that of the BP neural network; the improved RBF neural network has a high predicating accuracy, the average relative predication error is only 7.9%. It has a reference value for the similar rock mechanics problem.


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