Uncertainty Quantification of Store-Separation Simulation Due to Ejector Modeling Using a Monte Carlo Approach with Kriging Model (Advances in Applied Mathematics and Mechanics, 2022)
Precise calculation of the trajectory of store separation is critical in assessing whether the store can be released safely. Store ejection is the initial stage of thereleasing process and any uncertainty introduced at this stage will propagate throughthe whole trajectory. In this work, the impact of the uncertainties in ejector modelingon the simulation of a generic store separation is investigated by using a Monte-Carlo-based approach. To reduce the extremely large computation cost resulted from thedirect use CFD in Monte Carlo simulation, the CFD solutions are represented by atime-dependent Kriging model, which is constructed at each time step by using thesamples from the URANS simulations. The stochastic outputs, including the distribution of probability density function, expected value and 95% confidence interval ofstore separation trajectory, are obtained by the Monte Carlo simulations. The sensitivity analysis is also carried out by using the Monte-Carlo-based method to determinethe most significant variables in ejector modeling, which affect the output uncertainty.Our results show that ejector modeling is one of the main uncertainty sources of storeseparation simulation and the approximation in ejector modeling can cause a significant deviation, especially in the angular displacement.