(untitled) (Communications in Computational Physics, 2024)
Hypersonic vehicles create high-temperature environments, thus causing air molecules to undergo chemical reactions. As a result, the nonlinearity of the mapping relationship between heat flux and inflow condition is strengthened. Accurately predicting the heat flux of real gases using machine learning methods relies on an amount of training data of real gas, but the computational cost is often unacceptable. To solve the issue stated, we propose a novel neural network named Physically Guided Neural Network based on Transfer Learning (TL-PGNN). We design a new network architecture to depict physical laws and use heat flow data of ideal gases to enhance the network’s capability to depict heat flow from only a few real gas samples. Experiments of sphere demonstrate that, compared to ordinary DNN and PGNN, applying TL-PGNN decreases the mean L1 error by 72.66% and 24.53% on the test set, respectively.