Dynamic site response analysis in the face of uncertainty–an approach based on response surface method

Author(s):  
Wenxin Liu ◽  
C. Hsein Juang ◽  
Qiushi Chen ◽  
Guoxing Chen
Author(s):  
Pravajyoti Patra ◽  
V Huzur Saran ◽  
SP Harsha

The operating clearance in a bearing influences friction, load zone size and fatigue life of a bearing. Hence, an effort is made to investigate the effect of radial internal clearance on the dynamical behavior of a cylindrical roller bearing system with an unbalance present in the system. The differential equations representing the dynamics of the cylindrical roller bearings have been derived using Lagrange’s equations and solved numerically using the fourth-order Runge-Kutta iterative method. The nonlinear vibration signature has been analyzed due to the clearance and the same is represented by various tools like Acceleration-time plots, Poincaré plots and FFT plots. The approximation method is used to calculate the load distribution and deformation of the individual rollers located at a different position in the load zone, for a preloading/interference fit and positive internal clearance. A response surface method is used to analyze the severity involved in the system due to the combined effect of independent variables like rotor speed, radial load, and radial internal clearance. The observations presented here are not only useful to diagnose the bearing health condition with respect to parametric effects but also exhibit their interactive effects on bearing performance.


2014 ◽  
Vol 134 (9) ◽  
pp. 1293-1298
Author(s):  
Toshiya Kaihara ◽  
Nobutada Fuji ◽  
Tomomi Nonaka ◽  
Yuma Tomoi

Materials ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 3552 ◽  
Author(s):  
Chun-Yi Zhang ◽  
Jing-Shan Wei ◽  
Ze Wang ◽  
Zhe-Shan Yuan ◽  
Cheng-Wei Fei ◽  
...  

To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributed collaborative generalized regression extremum neural network is proposed by absorbing the heuristic thoughts of distributed collaborative response surface method and the generalized extremum neural network, in order to improve the reliability analysis of blade-tip clearance with creep behavior in terms of modeling precision and simulation efficiency. In this method, the generalized extremum neural network was used to handle the transients by simplifying the response process as one extremum and to address the strong nonlinearity by means of its nonlinear mapping ability. The distributed collaborative response surface method was applied to handle multi-object multi-discipline analysis, by decomposing one “big” model with hyperparameters and high nonlinearity into a series of “small” sub-models with few parameters and low nonlinearity. Based on the developed method, the blade-tip clearance reliability analysis of an aeroengine high-pressure turbine was performed subject to the creep behaviors of structural materials, by considering the randomness of influencing parameters such as gas temperature, rotational speed, material parameters, convective heat transfer coefficient, and so forth. It was found that the reliability degree of the clearance is 0.9909 when the allowable value is 2.2 mm, and the creep deformation of the clearance presents a normal distribution with a mean of 1.9829 mm and a standard deviation of 0.07539 mm. Based on a comparison of the methods, it is demonstrated that the proposed method requires a computing time of 1.201 s and has a computational accuracy of 99.929% over 104 simulations, which are improvements of 70.5% and 1.23%, respectively, relative to the distributed collaborative response surface method. Meanwhile, the high efficiency and high precision of the presented approach become more obvious with the increasing simulations. The efforts of this study provide a promising approach to improve the dynamic reliability analysis of complex structures.


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