Webinar 050
Towards a reliable methodology for assessing overpressure in hydrogen vents
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Speaker(s): Josué Melguizo-Gavilanes
Organisation(s): Shell
Duration: 58 min
SUMMARY
We recognize that the oil, gas, and chemical industries are persistently exposed to the risk of accidental releases of flammable gases, which can result in severe consequences if ignition occurs after a delay. As hydrogen becomes increasingly attractive as an alternative energy carrier, it introduces unique safety challenges due to its high explosion potential. Ensuring safe operations therefore requires a solid understanding of gas explosion behaviour and the ability to predict explosion outcomes with confidence. With hydrogen production and use expected to expand significantly in both industrial and public environments, we see a clear need for robust and reliable venting guidelines. At present, no industry-accepted methodology exists for estimating overpressures resulting from deflagration or detonation following delayed ignition of unconfined and uncongested hydrogen clouds. In this work, we seek to fill this gap by building upon a methodology proposed in earlier studies. Our approach involves estimating the mass of hydrogen contributing to blast loading through dispersion analyses performed with standard consequence modelling tools (e.g., FRED, PHAST), supplemented by engineering judgment. We also evaluate the limitations of existing unconfined and uncongested deflagration and detonation models by comparing their predictions against available experimental data, which reveal significant under- and overestimations of overpressure. To address these shortcomings, we propose an updated methodology based on a scaling relationship that links vent release conditions—specifically pressure, vent diameter and exit velocity—to the measured flame speeds in non-uniform, non-quiescent flammable clouds. Finally, we test the updated methodology against a recent large-scale experimental campaign carried out by Air Products and show a significant improvement in the predictive capabilities of the model.
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