Frequency domain stochastic response of structural systems with uncertain parameters: Closed-form sensitivity

2013 ◽  
pp. 53-54 ◽  
Keyword(s):  
Closed Form ◽  
Frequency Domain ◽  
Structural Systems ◽  
Uncertain Parameters ◽  
Stochastic Response

Nonlinear Oscillator Stochastic Response and Survival Probability Determination via the Wiener Path Integral

2015 ◽  
Vol 1 (2) ◽  
Author(s):  
Yuanjin Zhang ◽  
Ioannis A. Kougioumtzoglou
Keyword(s):  
Closed Form ◽  
Path Integral ◽  
Survival Probability ◽  
Nonlinear Oscillator ◽  
Transition Probability ◽  
Computational Cost ◽  
Discrete Version ◽  
Stochastic Response ◽  
Transition Probability Density ◽  
Approximate Analytical Expression

A Wiener path integral (WPI) technique based on a variational formulation is developed for nonlinear oscillator stochastic response determination and reliability assessment. This is done in conjunction with a stochastic averaging/linearization treatment of the problem. Specifically, first, the nonlinear oscillator is cast into an equivalent linear one with time-varying stiffness and damping elements. Next, relying on the concept of the most probable path, a closed-form approximate analytical expression for the oscillator joint transition probability density function (PDF) is derived for small time intervals. Finally, the transition PDF in conjunction with a discrete version of the Chapman–Kolmogorov (C–K) equation is utilized for advancing the solution in short-time steps. In this manner, not only the nonstationary response PDF but also the oscillator survival probability and first-passage PDF are determined. In comparison with existing numerical path integral schemes, a significant advantage of the proposed WPI technique is that closed-form analytical expressions are derived for the involved multidimensional integrals; thus, the computational cost is kept at a minimum level. The hardening Duffing and the bilinear hysteretic oscillators are considered as numerical examples. Comparisons with pertinent Monte Carlo simulation (MCS) data demonstrate the reliability of the developed technique.

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