Explosion risk analysis (ERA) is an effective method to investigate potential accidents in hydrogen production facilities. The ERA suffers from significant hydrogen dispersion-explosion scenario-related parametric uncertainty. To better understand the uncertainty in ERA results, thousands of Computational Fluid Dynamics (CFD) scenarios need to be computed. Such a large number of CFD simulations are computationally expensive. This study presents a stochastic procedure by integrating a Bayesian Regularization Artificial Neural Network (BRANN) methodology with ERA to effectively manage the uncertainty as well as reducing the stimulation intensity in hydrogen explosion risk study. This BRANN method randomly generates thousands of non-simulation data presenting the relevant hydrogen dispersion and explosion physics. The generated data is used to develop scenario-based probability models, which are then used to estimate the exceedance frequency of maximum overpressure. The performance of the proposed approach is verified by analyzing the parametric sensitivity on the exceedance frequency curve and comparing the results against the traditional ERA approach.

爆炸风险分析（ERA）是调查制氢设施中潜在事故的有效方法。ERA存在着与氢弥散爆炸场景相关的重大参数不确定性。为了更好地理解ERA结果的不确定性，需要计算数千种计算流体动力学（CFD）方案。如此大量的CFD模拟在计算上是昂贵的。这项研究通过将贝叶斯正则化人工神经网络（BRANN）方法与ERA集成在一起，提出了一种随机过程，可以有效地管理不确定性并降低氢爆炸风险研究中的刺激强度。这种BRANN方法随机生成数千个非模拟数据，这些数据提供了相关的氢扩散和爆炸物理学。生成的数据用于开发基于场景的概率模型，然后将其用于估计最大超压的超出频率。通过分析超出频率曲线上的参数灵敏度并将结果与​​传统ERA方法进行比较，可以验证所提出方法的性能。