Computational Fluid Dynamics (CFD) tools have been increasingly employed for carrying out quantitative risk assessment (QRA) calculations in the process industry. However, these tools must be validated against representative experimental data in order to have a real predictive capability. As any typical accident scenario is quite complex, it is important that the CFD tool is able to predict combined release and ignition scenarios reasonably well. However, this kind of validation is not performed frequently, primarily due to absence of good quality data. For that reason, the recent experiments performed by FZK under the HySafe internal project InsHyde are important. These involved vertically upwards hydrogen releases with different release rates and velocities impinging on a plate in two different geometrical configurations. The dispersed cloud was subsequently ignited and pressures recorded. These experiments are important not only for corroborating the underlying physics of any large-scale safety study, but also for validating the important assumptions used in QRA.Blind CFD simulations of the release and ignition scenarios were carried out prior to the experiments to predict the results (and possibly assist in planning) of the experiments. The simulated dispersion results are found to correlate reasonably well with experimental data in terms of the gas concentrations. The overpressures subsequent to ignition obtained in the blind predictions could not be compared directly with the experiments as the ignition points were somewhat different, but the pressure levels were found to be similar. Simulations carried out after the experiments with the same ignition position as those in the experiments compared reasonably well with the measurements in terms of the pressure level. This agreement points to the ability of the CFD tool FLACS to model such complex scenarios well. Nevertheless, the experimental set-up can be considered to be small-scale and less severe than many accidents and real-life situations. Future large-scale data of this type will be valuable to confirm ability to predict large-scale accident scenarios.
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