Optimizing Thermal Control Systems in Space Craft Using Machine Learning Algorithms: Increasing Efficiency Through Artificial Intelligence

Abstract

Thermal control systems in spacecraft are crucial in their role of maintaining operational performance and protecting the sensitive equipment in the extreme thermal environments of space. These systems manage internal temperatures of the spacecraft by preventing overheating or freezing of spacecraft components, which would harm the mission. As modern space missions become more and more complicated, there is a need to optimize energy efficiency.

Traditional methods rely on fixed algorithms to regulate heat. However, the various thermal environments and requirements for each mission require methods that are more adaptive and efficient. Recently, Artificial Intelligence has seen significant advancements, which provides the opportunity to enhance the efficiency of these systems. AI-based techniques allow for dynamic optimization that can adjust to the changing conditions in space and reduce the power
consumption.

The goal of this study is to explore how the usage of AI can improve the energy efficiency of spacecraft thermal control systems by optimizing heating power. Specifically, this research compares three AI algorithms—Gradient Descent, Genetic Algorithm, and Reinforcement Learning—across four different spacecraft types: LEO satellites, Geostationary Earth Orbit (GEO) satellites, Lunar Landers, and Deep Space Probes. By comparing heating power, speed of convergence, and error in optimization, this paper seeks to answer the following question: How can space-related missions improve energy efficiency and effectiveness through software that better manages thermal control systems in spacecraft?