Multidisciplinary uncertainty propagation method considering correlated field variables for rocket systems

Throughout a rocket's lifecycle, numerous random uncertainties can significantly influence performance. However, existing uncertainty propagation (UP) methods for multidisciplinary systems often neglect correlations among field variables, leading to reduced accuracy. To overcome this limitation, we propose a multidisciplinary UP method that explicitly incorporates these correlations. For variables propagated from upper-level disciplines, the Nataf transformation is applied to generate correlated input samples for the current discipline, which then serve as the basis for uncertainty analysis. To accelerate the calculation of the probability density function of field variables within the Nataf transformation, we further introduce a warm-start strategy integrated with the maximum entropy method. In the case study of UP across multiple disciplines of a solid rocket system, using Monte Carlo simulation (MCS) as the benchmark, incorporating variable correlations yields notable improvements: the standard deviation accuracy of velocity and total energy at the first-stage separation point increased by 22.75 % and 32.57 %, respectively, while the accuracy of their lower bounds improved by 5.20 % and 4.20 %. These results demonstrate that the proposed method effectively addresses UP problems involving both numerical and field correlated variables, significantly enhancing the accuracy of UP.