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 or-der 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 tem-perature distribution after the jet fire accident caused by the high-pressure hydrogen leakage from HRS.The accuracy of the predic-tion results of the surrogate model was quantitatively analyzed us-ing the fuzzy C-means and the Structure Similarity Index Mea-sure,and the results showed that the surrogate model for predict-ing the consequences of hydrogen jet fires can greatly improve the efficiency of consequence prediction under the premise of guaran-teeing the acceptable prediction accuracy,realizing the spatio-temporal and fast prediction of the consequences of hydrogen jet fires in HRS.
关键词
神经网络/代理模型/后果预测/加氢站/氢喷射火
Key words
neural network/surrogate model/consequence predic-tion/hydrogen refueling station/hydrogen jet fire
维普
万方数据