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Multi-Element Trajectory Models for Satellite Tour Missions

机译:卫星旅游任务的多元素轨迹模型

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Polynomial chaos expansions (PCEs) are useful for improving tractability and reducing computational cost when propagating uncertainty through nonlinear system dynamics. However, global PCEs may fail when applied to systems that exhibit non-smooth, multi-modal, or discontinuous behavior. Multi-element expansion models can achieve better performance for such systems by decomposing the random input space and generating individual PCEs for each component element. This study uses an adaptive approach to generating multi-element PCEs for the modeling of uncertainty propagation both for a spacecraft in a Molniya orbit about the Earth and for a spacecraft conducting a double-flyby during a satellite tour at an outer planet. It is shown that, in these applications, the multi-element models can achieve much more rapid convergence in the final state statistics compared to a Monte Carlo simulation and 1-2 orders of magnitude improvement in accuracy over a global PCE.
机译:多项式混沌扩展(PCE)可用于提高易无法解决,并通过非线性系统动态传播不确定性时降低计算成本。但是,当应用于展示非平滑,多模态或不连续行为的系统时,全局PCE可能会失败。通过将随机输入空间分解并为每个组件元素生成各个PCE来实现多元素扩展模型对这种系统来实现更好的性能。该研究利用自适应方法来产生多元件PCE,以便在围绕地球的Molniya轨道上的航天器和航天器在外部行星处的卫星之旅中进行双重飞行的航天器的不确定性传播的建模。结果表明,在这些应用中,与蒙特卡罗模拟相比,多元素模型可以在最终状态统计中实现更快的收敛性,并在全球PCE上精确地提高1-2次数量级改善。

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