基于不确定性的飞行器分离可靠性建模与分析方法

为实现飞行器分离任务可靠性的定量分析和高效精确评估，研究了高超声速飞行器分离任务过程中各种不确定性因素对分离可靠性的影响，提出一种基于不确定性的飞行器分离可靠性建模与分析方法。面向高超声速飞行器分离任务需求，建立分离动力学仿真模型，综合考虑分离过程不确定性因素的影响，利用灵敏度分析方法识别主要不确定性因素，构建分离可靠性模型。针对此模型，提出一种改进主动学习Kriging(IAK)的分离可靠性分析方法，通过新的采样策略选取失效概率更大的采样点作为新增训练点，进行高效可靠性分析。实例结果表明，该方法能够准确描述不确定性因素对分离过程的影响，提升分离可靠性定量分析的精度和效率，为飞行器分离方案的精细化设计提供支撑。

Uncertainty Based Reliability Modeling and Analysis Method ofFlight Vehicle Separation

 In order to evaluate the hypersonic vehicle separation reliability efficiently, the influence of the separation process uncertainties on the hypersonic vehicle separation reliability is studied and a new uncertainty-based reliability modeling and analysis method for the hypersonic vehicle separation is proposed. In this method, the kinetic model is constructed based on the hypersonic vehicle separation task requirements. The significant uncertainty parameters are identified through the sensitivity analysis method for the construction of the separation reliability model. To improve the efficiency of the separation reliability evaluation, an improved active Kriging (IAK) method is proposed in which the point that has a higher potential failure probability by a new sampling strategy is regarded as the next training point. It is shown that the influence of the uncertainties can be described exactly, and the accuracy and efficiency of the separation reliability analysis are improved, which can further support the detailed design of the vehicle separation.