Underground gas pipeline explosion and fire: CFD based assessment of foreseeability

• Underground natural gas pipeline accident is investigated.
• Semi-empirical and advanced CFD modelling are used to foresee the hazard.
• The estimated dispersion extent of ∼0.3 km favors the evidences.
• Safety distances from large methane fires were estimated as ∼2.5 km.
• Recommendations for avoiding such incidents in future are provided.

Scenarios of underground gas pipeline failure, crater formation, dispersion of gas, explosion and subsequent fires are investigated with semi-empirical and with CFD (Computational Fluid Dynamics) modelling. In order to strengthen the accident based learning approaches present investigations are performed in the context of recent GAIL (Gas Authority of India Limited) natural gas pipeline incident occurred in India. The foreseeability of damages to lives of people and assets due to explosion overpressure and thermal radiation are assessed. The released gas is considered as slightly dense-than-air i.e. 1.5 times. Depending on the LFL (Lower Flammability Limit) of gas the dispersion diameter and heights are predicted which followed the visual evidences appropriately. The model was furthermore tested with an even denser medium and was found to be worked well there too. The estimated explosion overpressures with the standard methods and also with CFD reproduced the scenario nicely. The effects of congestion VBR (Volume Blockage Ratio) in form of vegetation on stable atmospheric boundary layer flow is analysed and its contribution towards turbulence and hazard enhancement is studied.It is found that the major source of fatalities was higher thermal radiation emitted by pool fires of methane. The estimated thermal safety distances clearly demonstrate the ignorance/under estimation of likelihood and consequence of such hazardous events. For such incidents CFD demonstrated a strong capability to assess the pre or/and post events foreseeabilities within a reasonable amount of time and with an acceptable level of accuracy meeting the industrial needs for risk analysis.