arXiv2025
Accurate thermal analysis is crucial for modern spacecraft, driving demand for reliable modeling tools. This research advances space thermal modeling by improving the simulation accuracy and efficiency of radiative heat transfer, the dominant mode of heat exchange in space. To this end, we incorporate diffuse reflectivity using the Gebhart method, which computes radiative exchange factors (REFs) from geometric view factors. The view factors, obtained via Monte Carlo ray tracing (MCRT), require post-processing to mitigate statistical errors. Critically, existing correction schemes cannot simultaneously enforce closure and reciprocity for open systems. This research addresses this gap by proposing two novel enforcement methods: (i) a least-squares optimization with non-negativity rectification (NNR) and small positive value avoidance (SPVA), and (ii) an iterative enforcement algorithm. To ensure consistency across different discretization levels, this work also introduces the multi-node surface model relations to formalize the connection between sub-face, face, and node representations of view factors and REFs. A simple case study demonstrates a substantial reduction in mean absolute error (MAE): the least-squares method achieves an 81% MAE reduction, while the iterative method offers the best balance of accuracy (56% MAE reduction) and computational efficiency. A second case study shows that including diffuse reflections decreases the steady-state temperature of a plate by $4^{\circ}C$, reinforcing that reflected radiation reduces net absorption. This work introduces and validates computationally efficient methods for integrating diffuse reflectivity into space thermal analyses and for consistently coupling multi-node surface radiative models. The results enable more accurate and robust thermal predictions for spacecraft systems.