Prediction of hydrogen jet diffusion based on physics-informed deep learning
Hydrogen is one of the most flammable and explosive fuels.Once leaked,it can easily diffuse and potentially cause a fire.Real-time and accurate prediction of hydrogen diffusion is es-sential for predicting spatial concentration,which enables fire pre-vention of hydrogen facilities.In this study,a physics-informed deep learning model was proposed to effectively and accurately predict hydrogen concentration and velocity using sparse sensor data.The dependency between sensor data was learned by the graph neural network,and the physical differential equations of hydrogen diffusion were solved by graph nodes.The computed re-siduals were then used to optimize the parameters of the deep learning model.Public experimental data was applied to validate the performance of our proposed model.The results show that compared with the existing methods,the proposed method not only has real-time capability,but also predicts hydrogen concen-tration and velocity more accurately.This study provides accurate and real-time concentration and velocity prediction for hydrogen diffusion,facilitating hydrogen fire prevention.
new energyfire preventionhydrogendeep learninggraph neural network
万方数据
维普
