A Database Extension for a Safety Evaluation of a Hydrogen Refueling Station with a Barrier Using a CFD Analysis and a Machine Learning Method

Abstract

A methodology is proposed to extend datasets in a database suitable for use as a reference tool to support an evaluation of damage mitigation by a barrier wall in a hydrogen refueling station (HRS) during a vapor cloud explosion (VCE) accident. This is realized with a computational fluid dynamic (CFD) analysis and machine learning (ML) technology because measured data from hydrogen explosion tests with various installed barrier models usually require considerable amounts of time, a secured space, and precise measurements. A CFD sensitivity calculation was conducted using the radXiFoam v1.0 code and the established analysis methodology with an error range of approximately ±30% while changing the barrier height from that was used in an experiment conducted by the Stanford Research Institute (SRI) to investigate the effect of the barrier height on the reduction in peak overpressures from an explosion site to far fields in an open space. The radXiFoam code was developed based on the open-source CFD software OpenFOAM-v2112 to simulate a VCE accident in a humid air environment at a compressed gaseous or liquefied HRS. We attempted to extend the number of datasets in the VCE database through the use of the ML method on the basis of pressure data predicted by a CFD sensitivity calculation, also uncovering the possibility of utilizing the ML method to extend the VCE database. The data produced by the CFD sensitivity calculation and the ML method will be examined to confirm their validity and applicability to hypothetical VCE accident simulations if the related test data can be produced during experimental research. The database constructed using core data from the experiment and extended data from the CFD analysis and the ML method will be used to increase the credibility of radXiFoam analysis results for VCE accident scenarios at HRSs, ultimately contributing to safety assurances of HRSs in Republic of Korea.