The accuracy and reliability of Life Support System (LSS) simulations, such as V-HAB (Virtual Habitat), are crucial for the design and operation of future space habitats and long-duration missions. To bridge the gap between simulated and real space environment, virtual sensor models were incorporated into V-HAB. These models enable the simulation of realistic sensor characteristics and anomalies, accounting for noise, drift and other imperfections inherent in physical sensors. By integrating virtual sensor, the simulation output will more closely resemble actual system behavior, paving the way for digital twin applications, including the development of a small-scale LSS at the University of Stuttgart. Additional research was conducted to investigate the feasibility of Machine Learning (ML) based virtual sensors to replace their physical counterpart, potentially reducing hardware cost and increasing system flexibility. The ML sensors were trained on real data to predict the measurement of their physical counterparts by leveraging information from other available sensors. This design increases redundancy or allows substitution of a physical sensor when needed. By integrating virtual sensor models into V-HAB and studying the potential of ML-based sensors, this study aims to enhance realism and predictive capabilities of LSS simulations such as V-HAB, contributing to the development of more efficient, resilient and autonomous LSSs for future space exploration missions.
Christian Mayer, Amrutha Sriram, Claas Olthoff· 55th International Conferenc...· 0 citations
Human spaceflight represents a vital part of modern space exploration. The Artemis program and planned Gateway space station will soon push the boundaries of classical space exploration, necessitating innovative solutions to overcome the novel challenges of Life Support System (LSS) operation in extraterrestrial environments. Advanced simulation tools can facilitate the development and testing of LSSs within an environment that closely resembles actual mission conditions. This work presents the current development progress on a virtual model of the Columbus Science Laboratory onboard the International Space Station, specifically focusing on the Environmental Control and Life Support System as well as the Thermal Control System. The model was created using the simulation tool V-HAB (Virtual Habitat) and by performing a parameter identification study for the Condensing Heat Exchanger configuration. An initial test successfully demonstrated the methodology and enabled future development of a high-fidelity model. Additionally, the potential of integrating Machine Learning (ML) models for time series prediction was explored. The application of ML methods enables predictive maintenance, optimized resource allocation and enhanced system resilience, ultimately supporting the development of more efficient and reliable technologies.
Christian Mayer, Jannik Anwander, Claas Olthoff· 55th International Conferenc...· 0 citations
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