基于物理信息深度学习的氢气泄漏扩散预测

Prediction of hydrogen jet diffusion based on physics-informed deep learning

氢气易燃易爆易扩散,一旦发生泄漏,极易引发火灾事故,氢气泄漏扩散预测对氢能火灾防控至关重要。为了实时准确预测氢气泄漏扩散后果,提出基于物理信息深度学习的氢气泄漏扩散预测模型,通过图神经网络学习监测数据之间的依赖特性,进一步在图节点直接求解氢气射流扩散的物理微分方程,计算残差约束图神经网络深度学习的参数优化过程,根据稀疏浓度监测数据实现扩散浓度和速度的秒级预测。通过公开试验数据对比研究了所提模型性能,结果表明,与现有方法相比,该模型不仅具有秒级预测能力,而且能够更准确地模拟氢气泄漏扩散浓度和速度,可为氢气泄漏火灾防控提供实时参考信息。

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.