受撞击事件影响的太空栖息地贝叶斯热数字孪生

中文摘要 AI 辅助

太空栖息地可能会遭遇微流星体撞击等破坏性事件，这会导致结构损坏和内部环境的热异常，因此需要具备弹性的环境控制与生命保障系统（ECLSS）。数字孪生为支撑弹性、 onboard决策制定以及感知不确定性的自主系统提供了有前景的范式，但目前针对ECLSS热方面的数字孪生研究有限。本研究开发了一种用于遭受撞击诱导热异常的栖息地网络物理测试台的贝叶斯热数字孪生。我们构建了耦合热阻-热容网络模型，该模型表示物理和网络热子系统，并嵌入基于物理的激活函数以实现模型选择自动化、自适应和健康状态估计。使用实验温度数据对物理子系统进行离线贝叶斯校准；对于网络子系统，确定结构保护层厚度减少是撞击敏感参数。在固定物理参数和不敏感参数后，持续进行贝叶斯推理以估计与撞击相关的网络参数，从而实现撞击位置、时间和严重程度的检测。合成研究检验了超参数选择、可观测性和噪声影响，最终框架通过实验测试台数据进行验证。结果表明，所提出的数字孪生能够检测撞击诱导的热异常、推断具有量化不确定性的撞击相关参数、生成有用的温度预测，并为栖息地自主运行提供临界时间估计支持。

英文摘要

Space habitats may experience disruptive events, such as micro-meteorite impacts, that can induce structural damage and thermal anomalies in the interior environment, requiring resilient Environmental Control and Life Support Systems (ECLSS). Digital twins offer a promising paradigm for supporting resilience, onboard decision making, and uncertainty-aware autonomy. However, limited work has developed digital twins for the thermal aspects of ECLSS. This work develops a Bayesian thermal digital twin for a habitat cyber-physical testbed experiencing impact-induced thermal anomalies. We construct a coupled thermal resistance-capacitance network model representing the physical and cyber thermal subsystems and embed physics-based activation functions to automate model selection, enable adaptation, and facilitate health-state estimation. Offline Bayesian calibration is performed for the physical subsystem using experimental temperature data. For the cyber subsystem, reduction in structural protective layer thickness is identified as the impact-sensitive parameter. After fixing the physical parameters and insensitive parameters, Bayesian inference is performed continuously to estimate impact-relevant cyber parameters, enabling detection of impact location, timing, and severity. Synthetic studies examine hyperparameter selection, observability, and noise effects, and the final framework is validated using experimental testbed data. Results show that the proposed digital twin can detect impact-induced thermal anomalies, infer impact-relevant parameters with quantified uncertainty, generate informative temperature forecasts, and support time-to-critical estimation for autonomous habitat operation.

发表机构

• Purdue University（普渡大学）
• UT San Antonio（圣安东尼奥大学）

机构由 AI 辅助整理，请以论文原文为准。