Intelligent design of spacecraft functionally gradient structures for thermal-mechanical performance regulation

Against the background of precision payload mounting plates with complex heat sources in high-precision and high- stability spacecraft, an intelligent design method for functionally graded structures based on physics-informed neural networks is presented. Independent of thermal control devices, the local temperature in the load installation area and the fundamental frequency of the structure are regulated by optimizing the material gradient distribution. A loss function is established that includes the heat-conduction governing equation, local temperature and fundamental frequency optimization objectives, and material volume fraction constraints, considering multiple heat source distributions. The inverse problem with material gradient distribution as a design variable is solved using physics-informed neural networks. The results show that a reasonable design of the material gradient distribution can accurately regulate the local temperature at specific locations and the fundamental frequency without relying on external thermal control devices. The proposed method is expected to reduce the need for thermal vibration control devices for spacecraft structures and improve structural thermal-mechanical stability.