Probabilistic engineering analysis and design under time-dependent uncertainty

Time-dependent uncertainties, such as time-variant stochastic loadings and random deterioration of material properties, are inherent in engineering applications. Not considering these uncertainties in the design process may result in catastrophic failures after the designed products are put into operation. Although significant progress has been made in probabilistic engineering design, quantifying and mitigating the effects of time-dependent uncertainty is still challenging. This dissertation aims to help build high reliability into products under time-dependent uncertainty by addressing two research issues. The first one is to efficiently and accurately predict the time-dependent reliability while the second one is to effectively design the time-dependent reliability into the product. For the first research issue, new time-dependent reliability analysis methodologies are developed, including the joint upcrossing rate method, the surrogate model method, the global efficient optimization, and the random field approach. For the second research issue, a time-dependent sequential optimization and reliability analysis method is proposed. The developed approaches are applied to the reliability analysis of designing a hydrokinetic turbine blade subjected to stochastic river flow loading. Extension of the proposed methods to the reliability analysis with mixture of random and interval variables is also a contribution of this dissertation. The engineering examples tested in in this work demonstrate that the proposed time-dependent reliability methods can improve the efficiency and accuracy more than 100% and that high reliability can be successfully built into products with the proposed method. The research results can benefit a wide range of areas, such as life cycle cost optimization and decision making --Abstract, page iv