Chi‐Yang Li, Zihao Wang, Omar Al-Douri, Tylee L. Kareck, Angel Wileman, Abdul Raouf Tajik, Alexandra M. Schluneker, Debashis Basu, Qingsheng Wang
With the global shift towards sustainable energy, hydrogen and hydrogen-blended natural gas (HBNG) pipelines are becoming integral components of low-carbon energy infrastructures. Nevertheless, the unique characteristics of hydrogen, such as low ignition energy, a wide flammability range, and high diffusivity, introduce significant safety challenges, particularly in scenarios involving pipeline releases. This review systematically examines recent advancements in understanding, simulating, and predicting safety impacts associated with pipeline releases, and focuses on consequences of dispersion, fire, and explosion hazards. It synthesizes findings from experimental analyses, computational fluid dynamics (CFD)-based numerical simulations, and emerging machine learning methodologies. Experimental investigations elucidate specific leakage behaviors and identify critical safety thresholds, whereas numerical simulations expand understanding of hazards by evaluating various operational parameters. Machine learning approaches offer considerable potential for fast and precise prediction of hazard scenarios. By highlighting existing research gaps, this review underscores the need for integrated predictive models and comprehensive safety frameworks for hydrogen and HBNG pipeline infrastructures.