High-dimensional uncertainty quantification for Mars atmospheric entry using adaptive generalized polynomial chaos

The probabilistic uncertainties in Mars atmospheric entry degrade the entry guidance performance. The propagation law of high-dimensional uncertainty during Mars atmospheric entry is still an open problem that should be investigated. The current work aims to examine the uncertainty propagation during Mars atmospheric entry due to uncertain initial state and model parameters, with introducing the generalized polynomial chaos method into Mars atmospheric entry dynamics simulations. For more efficient and accurate, generalized polynomial chaos is modified through spectral decomposition and random space decomposition. First, stochastic dynamics are modeled and transformed into equivalent deterministic dynamics in a higher-dimensional space and are updated adaptively when the statistic characteristic of the system state changes greatly. Second, the random space is decomposed when the relative error in variance becomes larger than the predefined threshold. In each random sub-domain, the updated generalized polynomial chaos is employed. Finally, the adaptive generalized polynomial chaos is used to quantify the uncertainty propagation in Mars atmospheric entry dynamics. Comparison studies are also performed with traditional generalized polynomial chaos and Monte-Carlo simulations. The influence levels and the evolution profiles of the initial and parametric uncertainties are revealed through numerical simulations.