Abstract: As one of the most important supporting infrastructures for the hydrogen energy application industry, hydrogen refueling station (HRS) is characterized by the presence of large quantities of high-pressure hydrogen accompanied by significant leakage risks. Once the high-pressure hydrogen leaks, it is very easy to form a jet fire, which poses a serious threat to the structures in the HRS as well as to the safety of people's lives and properties. In order to realize the fast and accurate prediction of the consequences of hydrogen jet fire accidents, a neural network-based surrogate model accident consequence prediction method is proposed, which has a significant time-saving advantage over the traditional numerical simulation methods. The method constructs a hybrid surrogate model based on adversarial generative network and long and short-term memory neural network, and the training samples generated by numerical simulation are used to train the surrogate model, and the completed surrogate model can predict the temperature distribution after the jet fire accident caused by the high-pressure hydrogen leakage from HRS. The accuracy of the prediction results of the surrogate model was quantitatively analyzed using the fuzzy C-means and the Structure Similarity Index Measure, and the results showed that the surrogate model for predicting the consequences of hydrogen jet fires can greatly improve the efficiency of consequence prediction under the premise of guaranteeing the acceptable prediction accuracy, realizing the spatio-temporal and fast prediction of the consequences of hydrogen jet fires in HRS.

Key words: neural network, surrogate model, consequence prediction, hydrogen refueling station, hydrogen jet fire