文献类型：期刊文献

英文题名：Sensor-efficient, data-driven estimation of hydrogen leak source terms for indoor industrial and community systems

作者：Li, Ang[1];Lang, Ziqiang[1,2];Ni, Chuantao[1];Wang, Bing[1];Cao, Chenxi[1];Du, Wenli[1];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Univ Sheffield, Sch Elect & Elect Engn, Sheffield S1 3JD, England

年份：2026

卷号：252

外文期刊名：INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

收录：;EI(收录号：20262620979305);WOS:【SCI-EXPANDED(收录号:WOS:001807883200001)】；

基金：This work is supported by the National Natural Science Foundation of China (Basic Science Center Program 61988101, Key Program Grant 62303186 and Grant 62203173). The data for Experiment 2 is provided by Professor Li Xuefang from Shandong University.

语种：英文

外文关键词：Hydrogen leakage; Source term estimation; Data-driven; Limited number of sensors

摘要：Hydrogen is central to a low-carbon energy future, yet its high diffusivity and low density increase the risk of leakage, accumulation in confined spaces, and explosion. Accurate source term estimation (STE) based on sensor data is therefore critical for safe hydrogen operations. However, conventional STE methods require more sensors than potential leak sources-an assumption impractical for hydrogen systems, where extensive infrastructure and hydrogen's small molecular size create far more possible leak points than deployable sensors. Here, we present a sensor-efficient, data-driven STE framework that enables reliable leak localization and quantification under sparse sensing conditions. By integrating physics-informed modeling with data-driven inference, the method reconstructs leak source terms from limited hydrogen concentration measurements. The framework is validated using both high-fidelity computational fluid dynamics simulations of a hydrogen-fueled turbine generator and controlled experiments in a fuel cell vehicle garage. Remarkably, the STE is theoretically achievable with as few as two sensors, significantly reducing sensing requirements compared to existing approaches. This work provides a practical and scalable solution for real-time hydrogen leak source reconstruction in indoor industrial and community environments, advancing safety management and supporting the broader deployment of hydrogen energy systems.