Adaptive Uncertainty Propagation for Coupled Multidisciplinary Systems

This paper presents a novel uncertainty propagation approach for multidisciplinary systems with feedback couplings, model discrepancy, and parametric uncertainty. The proposed method incorporates aspects of Gibbs sampling, importance resampling, and density estimation to ensure that, under mild assumptions, the current method is provably convergent in distribution. The method uses the samples available from previously simulating the disciplines by applying sequential importance resampling. The absence or lack of samples in each discipline is addressed by introducing an adaptive greedy sample increment process to improve the efficiency of uncertainty analysis with minimum possible computational cost. A key feature of the approach is that disciplinary models are all synthesized independently based on their available data, and it does not require any full coupled system-level evaluations. The proposed approach is illustrated on the propagation of uncertainty for an aerodynamics–structures system and is compared to a system-level Monte Carlo uncertainty analysis approach.