Thermal parameter determination in underdetermined spacecraft thermal models using surrogate modeling

This study introduces a data-driven surrogating modeling approach for determining thermal parameters in undetermined thermal mathematical models (TMM) used in spacecraft systems. Maintaining appropriate temperature levels for onboard components is crucial for the success and safety of space missions. However, accurately determining thermal parameters in the undetermined TMM presents a challenge. To address this, we propose an approach that combines TMM correlation with test data and data-driven surrogate modeling. Our approach minimizes an objective function that measures the deviation between predicted temperatures from a surrogate model, incorporating thermal parameters, and steady state temperatures obtained from thermal vacuum balance test results. This optimization is performed using a genetic algorithm while imposing constraints to ensure the physical validity of the thermal parameters and correlation criteria related to temperatures. We demonstrate the effectiveness and validity of our approach by applying the obtained thermal parameters to the TMM. The resulting calculated temperatures exhibit excellent agreement with the test results. This methodology offers a user-friendly and reliable way to determine thermal parameters in undetermined TMM within spacecraft systems, ultimately enhancing temperature control and mission success.