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 (http://www.hysafe.org(link is external)) 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.