A Machine Learning Rapid Prediction of the Aerothermodynamic Environment for Near-Space Hypersonic Unmanned Aircraft

Abstract

Near-space hypersonic unmanned aircrafts (NHUA) encounter significant aerodynamic heating effects when flying at high velocities in extreme conditions. This leads to the generation of extremely high temperatures, reaching several thousand degrees, posing a substantial risk to the safety of NHUA. Accurate and rapid prediction of the aerothermodynamic environment is crucial for the thermal protection of NHUA. Conventional approaches exhibit some limitations, including the need for extensive pre-processing, long calculation time, inadequate precision, and reliance on expert knowledge, making them ill-suited for online intelligent prediction. This study proposes a novel “flying state-pressure and heat flux-temperature” data-driven prediction theoretical framework, considering both efficiency and accuracy. Our approach entails a prediction model for high-dimensional pressure and heat flux fields, employing principal component analysis (PCA) and multi-layer perceptron (MLP) models. A temperature time series model is also constructed using recurrent neural networks (RNN). The experimental results suggest that the prediction error falls within a narrow margin of approximately 5%. It takes around 0.1 seconds to forecast a high-dimensional field and 1 second to predict the temperature time series, which satisfies both speed and accuracy requirements.