| In certain situations, uncertainties of system properties and environmental conditions may significantly affect behavior of dynamical systems. Probabilistic risk and reliability analysis has been developed for quantitative analysis of such effects stemming from uncertainties.; The most widely used approach for uncertainty analysis is the Monte Carlo simulation-based method due to its versatility. This method, is usually computationally expensive. Such approach therefore may become infeasible in practice for large-scale system simulation. To circumvent or at least alleviate this limitation, numerous approximate analytical methods are developed to reduce the required computational burden.; This thesis reports an integrated approach involving high performance computing coupled with efficient simulation schemes to carry out dynamic simulations of uncertain systems.; Feasibility of the methodology is explored using a simple linear dynamical system with random properties defined by one-dimensional stochastic partial differential equations. |
