Prediction and interpretability of accidental explosion loads from hydrogen-air mixtures using CFD and artificial neural network method

Accurate prediction of blast loading from accidental hydrogen-air cloud explosion is critical for the planning, design, and operation of the hydrogen industry. This study proposes an efficient and accurate prediction model of explosion loads from hydrogen-air cloud explosions in vented silos. Based on the Bayesian regularization and data augmentation, an artificial neural network (ANN) model is generated and trained with the data collected from computational fluid dynamics (CFD) simulations using FLACS. The influences of various parameters including the silo dimensions (diameter and height), the ventilation size (width and length), and the ventilation panel activation pressures are considered. Analysis shows that the R2 value of the ANN model predictions and CFD simulation results exceeds 0.99. The developed ANN model could provide almost instantaneous prediction of the peak overpressure, the rise rate of overpressure, and the impulse for constructing the blast loading time history, which can be used in the design analysis and safety assessment of structures exposed to hydrogen explosions. Additionally, the model interpretability was conducted to analyse the importance and contribution of the input variables, which also verifies the reliability of the prediction model.