A Novel Galerkin Projection Approach for Damped Stochastic Dynamic Systems

Abstract eng:
This article provides the theoretical development and simulation results of a novel Galerkin subspace projection scheme for damped dynamic systems with stochastic coefficients. The fundamental idea involved here is to solve the stochastic dynamic system in the frequency domain by projecting the solution into a reduced finite dimensional spatio-random vector basis to approximate the response. A Nueumann expansion type of approach is used to generate the complex stochastic basis functions. The proposed method is applicable to linear dynamic systems with Gaussian and non-Gaussian random fields. Galerkin weighting coefficients have been employed to minimize the error induced due to the reduced basis and finite order spectral functions and hence to explicitly evaluate the stochastic system response. The statistical moments of the solution have been evaluated at all frequencies to illustrate and compare the stochastic system response with the deterministic case. The results have been compared to direct Monte-Carlo simulation for different correlation lengths and variability of randomness.

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