Speaker
Description
The increasing reliance on Commercial Off-The-Shelf (COTS) components for small satellite propulsion and suborbital platforms, such as the Brazilian Suborbital Microgravity Platform (PSM), introduces significant vulnerabilities to space weather phenomena. This study investigates the intersection between heliophysics data and the operational reliability of electrospray propulsion systems and power processing units (PPU). While the PSM provides a high-quality microgravity environment for technology validation, its suborbital trajectory exposes payloads to variable ionospheric conditions and solar-induced radiation, which can lead to transient faults in unhardened COTS electronics.
We propose a framework where Machine Learning (ML) algorithms, trained on solar activity and magnetospheric data, are utilized to predict performance anomalies in propulsion telemetry collected during PSM flight campaigns. By correlating environmental heliophysics datasets with real-time housekeeping data—such as current stability in ion emitters—it is possible to distinguish between inherent hardware failures and externally induced environmental interference. Furthermore, this research aligns with international engineering protocols by applying European Cooperation for Space Standardization (ECSS) tailoring to define safety-critical margins for missions operating under high solar activity. The integration of predictive ML models into the systems engineering lifecycle enhances the sustainability of the suborbital economy by ensuring that recoverable payloads can withstand the dynamic challenges of the space environment.