scholarly journals A Probabilistic Assessment of the Causes of Active Deformation in the East Central Mediterranean Using Spherical Finite Element Models

2021 ◽  
Author(s):  
Rob Govers ◽  
Matthew Herman ◽  
Lukas van de Wiel ◽  
Nicolai Nijholt
1988 ◽  
Vol 16 (1) ◽  
pp. 18-43 ◽  
Author(s):  
J. T. Oden ◽  
T. L. Lin ◽  
J. M. Bass

Abstract Mathematical models of finite deformation of a rolling viscoelastic cylinder in contact with a rough foundation are developed in preparation for a general model for rolling tires. Variational principles and finite element models are derived. Numerical results are obtained for a variety of cases, including that of a pure elastic rubber cylinder, a viscoelastic cylinder, the development of standing waves, and frictional effects.


1997 ◽  
Author(s):  
Francois Hemez ◽  
Emmanuel Pagnacco ◽  
Francois Hemez ◽  
Emmanuel Pagnacco

2021 ◽  
pp. 107754632199759
Author(s):  
Jianchun Yao ◽  
Mohammad Fard ◽  
John L Davy ◽  
Kazuhito Kato

Industry is moving towards more data-oriented design and analyses to solve complex analytical problems. Solving complex and large finite element models is still challenging and requires high computational time and resources. Here, a modular method is presented to predict the transmission of vehicle body vibration to the occupants’ body by combining the numerical transfer matrices of the subsystems. The transfer matrices of the subsystems are presented in the form of data which is sourced from either physical tests or finite element models. The structural dynamics of the vehicle body is represented using a transfer matrix at each of the seat mounting points in three triaxial (X–Y–Z) orientations. The proposed method provides an accurate estimation of the transmission of the vehicle body vibration to the seat frame and the seated occupant. This method allows the combination of conventional finite element analytical model data and the experimental data of subsystems to accurately predict the dynamic performance of the complex structure. The numerical transfer matrices can also be the subject of machine learning for various applications such as for the prediction of the vibration discomfort of the occupant with different seat and foam designs and with different physical characteristics of the occupant body.


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