Xinmeng Zhang

Ms. Xinmeng Zhang

orcid

Research Focus & Metrics

Academic Career Length

1Year (2026-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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30 March 2026 in Energies
Verified
Journal Article
Open access
PDF file
XZXinmeng Zhang
Xinmeng Zhang
ZZZilong Zhang
Zilong Zhang
JSJiangtao Sun
Jiangtao Sun
YOYujie Ouyang
Yujie Ouyang
JZJing Zhang
Jing Zhang
LXLifeng Xie
Lifeng Xie
Against the backdrop of the global transition toward clean and low-carbon energy systems, hydrogen has emerged as a promising alternative to fossil fuels owing to its carbon-free characteristics and broad cross-sector applicability. However, the high diffusivity and wide flammability range of hydrogen pose significant safety challenges for its large-scale deployment. Conventional detection methods are generally limited to point-based data acquisition and struggle to capture the transient flow-field characteristics associated with hydrogen diffusion as well as combustion or explosion processes. This review aims to systematically clarify the exclusive technical advantages of schlieren visualization technology for hydrogen research, summarize its application progress in hydrogen and hydrogen mixture diffusion distribution and combustion/explosion flow-field testing, and propose future optimization directions and application expansion paths. Schlieren visualization, based on optical refraction principles, has evolved from a traditional experimental technique into a comprehensive system adapted to diverse scenarios, including high-speed schlieren, Z-type schlieren, background-oriented schlieren (BOS), and color schlieren. Owing to its non-intrusive nature, high spatiotemporal resolution and full-field visualization capability, schlieren technology can directly observe the fundamental diffusion behavior of hydrogen jets and capture distinctive flow features throughout all stages of hydrogen mixture combustion and explosion. It effectively overcomes the limitations of conventional detection methods and has become an indispensable tool in hydrogen energy safety research. Future research should focus on improving technical performance, strengthening interdisciplinary integration with machine learning and digital twin technologies, and expanding application scenarios to multi-field coupling systems, so as to support the safe and efficient development of the hydrogen industry and contribute to global carbon peaking and carbon neutrality goals.