Abstract
The paper presents a complete experimental validation of an advanced computational methodology adapted to the nonlinear post-buckling analysis of geometrically nonlinear structures in presence of uncertainty. A mean nonlinear reduced-order computational model is first obtained using an adapted projection basis. The stochastic nonlinear computational model is then constructed as a function of a scalar dispersion parameter, which has to be identified with respect to the nonlinear static experimental response of a very thin cylindrical shell submitted to a static shear load. The identified stochastic computational model is finally used for predicting the nonlinear dynamical post-buckling behavior of the structure submitted to a stochastic ground motion.
Original language | English (US) |
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Pages (from-to) | 210-230 |
Number of pages | 21 |
Journal | Computer Methods in Applied Mechanics and Engineering |
Volume | 271 |
DOIs | |
State | Published - Apr 1 2014 |
Keywords
- Dynamic post-buckling
- Experimental identification
- Geometrical nonlinearities
- Non-stationary stochastic excitation
- Static post-buckling
- Uncertainties
ASJC Scopus subject areas
- Computational Mechanics
- Mechanics of Materials
- Mechanical Engineering
- Physics and Astronomy(all)
- Computer Science Applications