主管:中华人民共和国应急管理部
主办:应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU

消防科学与技术 ›› 2024, Vol. 43 ›› Issue (5): 698-703.

• • 上一篇    下一篇

基于物理信息深度学习的氢气泄漏扩散预测

张新琪1, 师吉浩1, 陈国明2   

  1. (1. 香港理工大学 建筑环境与能源工程系,中国香港 999077;2. 中国石油大学(华东)海洋油气装备与安全技术研究中心,山东 青岛 266580)
  • 收稿日期:2024-01-25 修回日期:2024-03-07 出版日期:2024-05-15 发布日期:2024-05-15
  • 作者简介:张新琪(1995- ),女,河北保定人,香港理工大学建筑环境与能源工程系研究助理,主要从事油气泄漏灾变风险防控方面的研究,中国香港特别行政区九龙红磡育才道11号,999077。
  • 基金资助:
    国家重点研发计划(2021YFB4000901-03);国家自然科学基金(52101341)

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

Zhang Xinqi1, Shi Jihao1, Chen Guoming2   

  1. Beijing, Beijing 100083, China)
  • Received:2024-01-25 Revised:2024-03-07 Online:2024-05-15 Published:2024-05-15

摘要: 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。

关键词: 新能源, 火灾防控, 氢气, 深度学习, 图神经网络

Abstract: 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 essential for predicting spatial concentration, which enables fire prevention 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 residuals 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 concentration and velocity more accurately. This study provides accurate and real-time concentration and velocity prediction for hydrogen diffusion, facilitating hydrogen fire prevention.

Key words: new energy, fire prevention, hydrogen, deep learning, graph neural network