Temperature field reconstruction of on-orbit aircraft based on multi-source frequency domain information fusion

Temperature reconstruction is vital for ensuring system reliability in electronic component design. However, current approaches struggle to effectively explore system information and physical relationships, thereby limiting their performance. This paper presents deep learning surrogate models for precise temperature field reconstruction, showcasing their effective discernment of system distribution laws. However, the scarcity of high-quality training data poses a significant challenge, often leading to issues like overfitting and compromised precision. To address this problem, the paper proposes an adaptive multi-source information fusion method (MFIF) for integrating physical information from various data sources in the frequency domain. By leveraging frequency domain analysis, a deeper understanding of underlying physical phenomena is achieved, facilitating effective integration of information. Furthermore, by utilizing deep surrogate models and high-quality training samples, the developed multi-source frequency fusion method enables the creation of a multi-source fusion driven deep learning method for temperature field reconstruction. The proposed method enhances the robustness, accuracy, and effectiveness of aircraft temperature field reconstruction in orbit. Experimental results demonstrate a substantial decrease in both noise and errors, while the Signal-to-Noise Ratio can be improved by up to more than 86%.