This paper carries on a reliability sensitivity analysis on the non-ablative thermal protection system (TPS) of spacecraft during the conceptual design. In the previous work on probabilistic estimation of TPS, the temperature dependency of material properties has not yet been investigated. In this paper, however, the temperature dependency of material properties is characterized and considered during the thermal analysis and reliability sensitivity analysis. Compared to general black-box problems, three special challenges of uncertainty analysis for TPS in real practice are a generally high dimension and multiple outputs on massive meshing nodes, a high level of reliability design target, and a fast evaluation process due to the requirement of the conceptual design. In order to cope with these challenges, a unified reliability sensitivity analysis methodology including multi-input and multi-output support vector machines (MIMO-SVMs), a space-partition (SP) method, and a generalized subset simulation (GSS) is proposed for the conceptual design of TPS with temperature-dependent materials. MIMO-SVMs are used to approximate the thermal responses to save calculation costs. The variance-based global sensitivity indices are calculated by SP to make full use of the information within samples. Based on the sensitivity indices, a dimension reduction process is introduced. In the reduced space, GSS is used to simultaneously evaluate all the failure probabilities by fully exploring the correlation among all the LSFs. Two application examples including a lifting body vehicle model and a spacecraft model are used to demonstrate the performance of the proposed methodology.

本文在概念设计过程中对航天器非烧蚀热保护系统（TPS）进行了可靠性敏感性分析。在以前的TPS概率估计工作中，尚未研究材料特性的温度依赖性。然而，在本文中，在热分析和可靠性敏感性分析过程中，对材料性能的温度依赖性进行了表征和考虑。与一般的黑匣子问题相比，实际中对TPS进行不确定性分析的三个特殊挑战是：高维和大规模网格化节点上的多个输出，高级别的可靠性设计目标以及由于以下要求而导致的快速评估过程：概念设计。为了应对这些挑战，针对带温度的TPS的概念设计，提出了包括多输入多输出支持向量机（MIMO-SVM），空间划分（SP）方法和广义子集仿真（GSS）的统一可靠性敏感性分析方法。依赖的材料。MIMO-SVM用于近似热响应，以节省计算成本。SP计算基于方差的全局敏感性指数，以充分利用样本中的信息。基于灵敏度指标，引入了降维过程。在缩小的空间中，GSS用于通过充分探索所有LSF之间的相关性来同时评估所有故障概率。