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首页> 外文期刊>Aerospace science and technology >Breakup prediction under uncertainty: Application to upper stage controlled reentries from GTO orbit
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Breakup prediction under uncertainty: Application to upper stage controlled reentries from GTO orbit

机译：不确定性下的分发预测：从GTO轨道应用到上阶段控制重返的应用

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More and more human-made space objects re-enter the atmosphere, and yet the risk for human populations remains often unknown because predicting their reentry trajectories is formidably complex. While falling back on Earth, the space object absorbs large amounts of thermal energy that affects its structural integrity. It undergoes strong aerodynamic forces and heating that lead to one or several breakups. Breakup events have a critical influence on the rest of the trajectory but are extremely challenging to predict and subject to uncertainties. In this work, we present an original model for robustly predicting the breakup of a reentering space object. This model is composed of a set of individual solvers that are coupled together such as each solver resolves a specific aspect of this multiphysics problem. This paper deals with two levels of uncertainties. The first level is the stochastic modeling of the breakup while the second level is the statistical characterization of the model input uncertainties. The framework provides robust estimates of the quantities of interest and quantitative sensitivity analysis. The objective is twofold: first to compute a robust estimate of the breakup distribution and secondly to identify the main uncertainties in the quantities of interest. Due to the significant computational cost, we use an efficient framework particularly suited to multiple solver predictions for the uncertainty quantification analysis. Then, we illustrate the breakup model for the controlled reentry of an upper stage deorbited from a Geosynchronous Transfer Orbit (GTO), which is a classical Ariane mission. (C) 2019 Elsevier Masson SAS. All rights reserved.
机译：越来越多的人造空间物体重新进入大气，然而人口的风险仍然是未知的，因为预测其再入式轨迹是巨大的复杂性。在落在地球上时，空间物体吸收大量的热能，影响其结构完整性。它经历了强大的空气动力和加热，导致一个或多个分裂。分手事件对轨迹的其余部分具有危重影响，但对预测并受不确定性来说是非常具有挑战性的。在这项工作中，我们提出了一种原始模型，用于强大地预测重新进入空间对象的分解。该模型由一组单独的求解器组成，该载体耦合在一起，例如每个解算变解析该多体问题问题的特定方面。本文涉及两级不确定性。第一级是分手的随机建模，而第二级是模型输入不确定性的统计表征。该框架提供了对兴趣数量和定量敏感性分析的强大估计。目标是双重的：首先计算分配的稳健估计，其次是识别利息数量的主要不确定性。由于具有重要的计算成本，我们使用特别适用于多个求解器预测的有效框架，以进行不确定量化分析。然后，我们说明了从地球同步传输轨道（GTO）中吸收的上阶段的受控再入的分解模型，这是一种经典的Ariane任务。 （c）2019年Elsevier Masson SAS。版权所有。

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