Salt caverns are accepted as an ideal solution for high-pressure hydrogen storage. As well as considering the numerous benefits of the realization of underground hydrogen storage (UHS), such as high energy densities, low leakage rates and big storage volumes, risk analysis of UHS is a required step for assessing the suitability of this technology. In this work, a preliminary quantitative risk assessment (QRA) was performed by starting from the worst-case scenario: rupture at the ground of the riser pipe from the salt cavern to the ground. The influence of hydrogen contamination by bacterial metabolism was studied, considering the composition of the gas contained in the salt caverns as time variable. A bow-tie analysis was used to highlight all the possible causes (basic events) as well as the outcomes (jet fire, unconfined vapor cloud explosion (UVCE), toxic chemical release), and then, consequence and risk analyses were performed. The results showed that a UVCE is the most frequent outcome, but its effect zone decreases with time due to the hydrogen contamination and the higher contents of methane and hydrogen sulfide.

This paper proposes a methodology to perform risk analysis of the virus spread. It is based on the coupling between CFD modelling of bioaerosol dispersion to the calculation of probability of contact events. CFD model of near-field sneeze droplets dispersion is developed to build the SARS-CoV-2 effect zones and to adequately capture the safe distance. The most shared classification of droplets size distribution of sneezes was used. Droplets were modeled through additive heating/evaporation/boiling laws and their impact on the continuous phase was examined. Larger droplets move behind the droplet nuclei front and exhibit greater vertical drop due to the effect of gravity. CFD simulations provided the iso-risk curves extension i.e. the maximum distance as well as the angle enclosed by the incident outcome effect zone. To calculate the risk indexes, a fault tree was developed and the probability of transmission assuming as of the top event “COVID-19 infection” was calculated starting from the virus spread curve, as main base case. Four phases of virus spread evolution were identified: initiation, propagation, generalised propagation and termination. For each phase, the maximum allowable close contact was computed, being fixed the values of the acceptable risk index. In particular, it was found that during the propagation case, the maximum allowable close contacts is two, suggesting that at this point lockdown should be activated. The here developed methodology could drive policy containment design to curb spread COVID-19 infection.

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