Bayesian thermal digital twin for a space habitat subjected to an impact event

RESUMO

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.