Speaker
Description
Satellite thermal management systems are critical for maintaining the operational integrity of spacecraft, yet they are typically designed as reactive systems. This study presents a novel methodology for predictive thermal health monitoring using Physics-Informed Neural Networks (PINNs) to safeguard satellites against extreme space weather events. The primary objective is to differentiate between nominal thermal drift and anomalies induced by high-energy particle flux and geomagnetic disturbances.
The methodology integrates real-time satellite telemetry—specifically temperature data sourced from on-board Phase Change Material (PCM) thermal control units—with external space weather indicators, including solar proton flux and planetary geomagnetic indices. By training a PINN model on both physical thermal equations and historical space weather datasets, the system learns to predict the satellite's internal thermal state under varying environmental stressors. This approach allows for the identification of thermal excursions before they exceed safety thresholds.
Main results demonstrate that the PINN framework achieves higher predictive accuracy than standard statistical models by adhering to the governing thermodynamic constraints of the spacecraft. This research significantly improves mission assurance, offering a proactive tool for autonomous satellite operators to mitigate the risks of hardware overheating during solar events. The significance of this work lies in its interdisciplinary application, demonstrating how integrating material-level thermal expertise with machine learning can enhance the longevity and autonomy of small-satellite constellations in increasingly dynamic space environments.