发表机构
Innovation Academy for Microsatellites, Chinese Academy of Sciences; Shanghai Jiao Tong University（中国科学院微小卫星创新研究院; 上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对航天器热设计中组件数量和布局变化导致的温度场预测难题，提出树结构因子组合网络（TFCN），通过分解可重用局部因子并组合学习全局响应，在分布外场景下显著降低RMSE，提升预测可靠性。
AI 中文摘要
航天器热设计需要反复评估发热组件的数量和空间排列以及热边界条件的变化如何影响温度场。高保真数值模拟计算成本高昂，因此难以用于大规模设计筛选。尽管代理模型可以加速温度场预测，但现有方法通常将每个完整配置作为一个整体进行编码，并未显式利用局部物理组件在不同配置间的可重用性，这限制了它们在训练未覆盖的组件数量和组合上的准确性。为解决此问题，我们提出了树结构因子组合网络（TFCN），它将复杂的航天器热配置分解为可重用的局部物理因子，并采用树结构组合模块来学习与不同因子组合相关的全局温度场响应。TFCN在具有指定温度和辐射通量边界条件的二维稳态航天器热分析案例上进行了评估。模型仅在包含不超过15个发热组件的配置上进行训练，并在包含16-25个组件的未见配置上进行评估。对于指定温度和辐射通量案例，TFCN的组件数量分布外均方根误差（RMSE）分别为4.21 K和18.62 K，相对于最强基线分别降低了65.6%和33.6%。这些结果表明，TFCN提高了组件数量变化下温度场预测的可靠性，并为快速航天器热设计评估和大规模配置筛选提供了高效的代理模型。
英文摘要
Spacecraft thermal design requires repeated evaluation of how variations in the number and spatial arrangement of heat-generating components and in thermal boundary conditions affect the temperature field. High-fidelity numerical simulations are computationally expensive and therefore difficult to use for large-scale design screening. Although surrogate models can accelerate temperature-field prediction, existing approaches generally encode each complete configuration as a whole and do not explicitly exploit the reusability of local physical constituents across configurations, which limits their accuracy for component counts and combinations not covered during training. To address this issue, we propose the Tree-Structured Factor Composition Network (TFCN), which decomposes complex spacecraft thermal configurations into reusable local physical factors and employs a tree-structured composition module to learn the global temperature-field response associated with different factor combinations. TFCN is evaluated on two-dimensional steady-state spacecraft thermal-analysis cases with prescribed-temperature and radiative-flux boundary conditions. The model is trained exclusively on configurations containing no more than 15 heat-generating components and evaluated on unseen configurations containing 16-25 components. For the prescribed-temperature and radiative-flux cases, TFCN achieves component-count out-of-distribution RMSE values of 4.21 K and 18.62 K, respectively, representing reductions of 65.6% and 33.6% relative to the strongest baseline. These results demonstrate that TFCN improves the reliability of temperature-field prediction under variations in component count and provides an efficient surrogate for rapid spacecraft thermal-design evaluation and large-scale configuration screening.
Comments32 pages, 13 figures
