Preliminary Research on the Uncertainty Estimation in the Probabilistic Designs

In probabilistic design, the challenge is to estimate the uncertainty propagation, since outputs of subsystems at lower levels could constitute inputs of other systems or at higher levels of the multilevel systems. Three uncertainty propagation estimation techniques are compared in this paper in terms of numerical efficiency and accuracy: root sum square (linearization), distribution-based moment approximation, and Taguchi-based integration. When applied to reliability-based design optimization (RBDO) under uncertainty, it is investigated which type of applications each method is best suitable for. Two nonlinear analytical examples and one vehicle crashworthiness for side-impact simulation example are employed to investigate the unique features of the presented techniques for uncertainty propagation. This study aims at helping potential users to identify appropriate techniques for their applications in the multilevel design.