Prediction model for the evolution of hydrogen concentration under leakage in hydrogen refueling station using deep neural networks

Abstract

The widespread risks of leakages in the hydrogen industry chain require a method that can quickly predict the consequences of accidents, especially in the hydrogen refueling station (HRS). This paper presents a surrogate model based on physics-informed neural network (PINN) that can predict the distribution of hydrogen concentration after a leakage. The proposed Physics-informed Convolutional Long Short-Term Memory Network (PI-ConvLSTM) model improves the concentration prediction results at the gas cloud boundary by adding a physical constraint term to the loss function of the ConvLSTM model. The concentration distributions after hydrogen leakage at HRS simulated by FLACS are used as the training samples, and the concentration data are converted into grayscale maps for training. The hydrogen concentration prediction method with the proposed surrogate model as the core achieves fast prediction of the gas cloud concentration distribution with acceptable accuracy. It is observed that the method can greatly reduce the prediction time of the consequences of hydrogen leak accidents with the surrogate model already trained. It can provide real-time risk warning and consequence prediction for hydrogen refueling station leakage accidents.