Quantitative risk assessment of a hydrogen refueling station by using a dynamic physical model based on multi-physics system-level modeling

Abstract

Numerous accidents in HRSs have been reported worldwide in accident databases; therefore, many researchers have performed quantitative risk assessments (QRAs) of HRSs to enable risk-informed decision making in determining the safety distances or risk mitigation measures. The HRSs, located in urban areas such as Tokyo in Japan, are situated in congested areas with tall buildings and high population density; thus, they have relatively narrow station areas. However, the QRAs are generally suitable for large plants such as nuclear power plants or chemical plants; therefore, relatively small plants or installations, such as HRSs, have not yet been considered as QRA objects. Hence, it is necessary to conduct detailed QRAs with risk analyses and reduce the applied uncertainties for relatively small plants or installations. We applied a model-based approach of risk assessment to model the HRS process using multi-physics system-level modeling and simulated a target system using Modelica—an equation-based, object-oriented modeling language that allows acausal modeling of complex cyber-physical systems The primary aim of this study was to conduct a QRA of an HRS based on multi-physics system-level modeling. First, we modeled the HRS components and physical relationships between the components using basic physical equations. Then, we elucidate a QRA based on the constructed model. The difference in the leakage rates due to the leak positions and dynamic behavior of the model parameters were calculated using the constructed model. Finally, we estimated the individual risks of all the scenarios and compared the resulting risk contours based on the constructed model that includes the hydrogen-fuel dynamic behavior with those based on the traditional model. These results indicate that it is possible to assess whether the risks around the station boundary are acceptable based on the scenario information obtained by evaluating the risks near the station.