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🔥 Content Introduction
The CubeSat, as a representative of low-cost, modular small satellites, has been widely used in fields such as Earth observation, communication relay, and scientific experiments. However, the extreme thermal environment encountered during its orbit around the Earth poses a critical challenge affecting satellite performance and lifespan — the lack of atmospheric heat conduction and convection in space causes the satellite’s surface temperature to fluctuate dramatically between the “sunlit” and “shadow” zones, reaching over 100°C at its peak and dropping below -100°C at its lowest. Such extreme temperature differences can lead to component failures: for instance, lithium batteries significantly lose capacity at low temperatures, while high temperatures may cause electrolyte leakage; onboard computer chips may crash or produce errors if they exceed their operational temperature thresholds; structural materials may experience stress from thermal expansion and contraction, which can accumulate over time and lead to component detachment or seal failure.
Therefore, precise thermal modeling is a core aspect of CubeSat design and mission assurance. Through thermal modeling, the temperature variation patterns during the satellite’s entire orbital cycle can be simulated before launch, allowing for the early identification of high-temperature or low-temperature risk points, optimizing the thermal control system design (such as selecting appropriate thermal control coatings, arranging heaters or heat pipes), and ensuring that all components operate stably within the allowable temperature range. For example, a 6U CubeSat used for Earth observation (approximately 10cm×20cm×30cm) requires its imaging sensor to maintain a constant temperature environment of 20±5°C; thermal modeling can calculate the number of heat pipes and the power of heaters needed around the sensor to avoid temperature fluctuations affecting imaging accuracy.
⛳️ Operating Results
📣 Sample Code
norm1_local=[1; 0; 0];
norm2_local=[-1; 0; 0];
norm3_local=[0; 1; 0];
norm4_local=[0; -1; 0];
norm5_local=[0; 0; 1];
norm6_local=[0; 0; -1];
T=[cos(acend_node)*cos(w+true_anomoly)-sin(acend_node)*cos(i)*sin(w+true_anomoly)…
-cos(acend_node)*sin(w+true_anomoly)-sin(acend_node)*cos(i)*cos(w+true_anomoly)…
sin(acend_node)*sin(i);…
sin(acend_node)*cos(w+true_anomoly)+cos(acend_node)*cos(i)*sin(w+true_anomoly)…
-sin(acend_node)*sin(w+true_anomoly)+cos(acend_node)*cos(i)*cos(w+true_anomoly)…
-cos(acend_node)*sin(i);…
sin(i)*sin(w+true_anomoly) sin(i)*cos(w+true_anomoly) cos(i)];
n1=T*norm1_local;
n2=T*norm2_local;
n3=T*norm3_local;
n4=T*norm4_local;
n5=T*norm5_local;
n6=T*norm6_local;
🔗 References
[1] Liao Wenhe. Development and Application of CubeSat Technology [J]. Journal of Nanjing University of Aeronautics and Astronautics, 2015, 47(6):6. DOI:CNKI:SUN:NJHK.0.2015-06-002.
🎈 Some theoretical references are from online literature; please contact the author for removal if there is any infringement.
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