Probability Density Evolution Method for Uncertainty Propagation in Nonlinear Stochastic System

Abstract eng:
The uncertainties involved in the input information (e.g., the randomness in the excitations and the structural parameters) could be called the source of randomness (SOR), which will exhibit their effects in the output information. The uncertainty propagation could be captured on three different levels: the level of sample, of second order statistics and of PDF (Figure 1). Traditional methods for stochastic dynamics provide tools for the first and second level, whereas the probability density evolution method provides a tool for the third level (Li & Chen, 2008; 2009). An example is studied to illustrate the application of PDEM to charactering uncertainty propagation in nonlinear multi-degree-of-freedom systems (Figures 2 and 3). It is seen that the characteristics of PDF with SOR being excitation and structural parameter are quite different (Figure 3). Besides, the trends for inter-story drift and shear are far from linear and evolve oppositely.

• Export as: BibTeX | MARC | MARCXML | DC | EndNote | NLM | RefWorks
• Add to your basket