@Article{CiCP-36-651,
author = {Chen , BiaoZhang , JifaZhang , ShuaiSong , Xiaoxiao and Zheng , Yao},
title = {Physically Guided Neural Network Based on Transfer Learning (TL-PGNN) for Hypersonic Heat Flux Prediction},
journal = {Communications in Computational Physics},
year = {2024},
volume = {36},
number = {3},
pages = {651--672},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2023-0308},
url = {http://global-sci.org/intro/article_detail/cicp/23455.html}
}