Temperature Prediction for Aerospace Thermal Tests Based on Physical and LSTM Hybrid Model

During spacecraft operations, structures experience extreme aerodynamic heating, necessitating thermal testing to gather data on the thermal response of surface materials. Given the nonrepeatable nature of these test articles, accurately predicting the temperature rise profile under thermal load inputs is essential before formal testing. Although theoretical analyses can develop precise internal heat transfer models for modules, limited test data hampers the modeling of inter-module heat transfer processes. Furthermore, variations in test article parameters across different tests restrict the generalizability of existing models. We present a hybrid modeling approach that integrates a physical model with a long short-term memory (LSTM) network to address these challenges. The LSTM model is trained on historical data to capture complex inter-module heat transfer dynamics. Additionally, varying parameters of the test articles are included as model inputs to enhance versatility and adaptability. Experiments demonstrate that the model achieves high prediction accuracy (MAE = 17.41 (K) R2 = 0.9988) even when test article parameters differ from historical data. Moreover, it shows strong adaptability to changes in the input power signal (MAE = 34.91 (K) R2 = 0.9990). This study successfully predicts temperature profiles during thermal testing using minimal test data, thereby improving computational efficiency and reducing testing costs, which facilitates the effective implementation of formal thermal testing.