Performance analysis of an air quality CFD model in complex environments: Numerical simulation and experimental validation with EMU observations

- CFD model fluidyn-PANACHE is validated with two geometrically different cases of EMU experiment.
- The steady and unsteady RANS simulations in each EMU Case are performed and compared.
- The statistical evaluation methods are used for performance analysis of simulated results.
- The fluidyn-PANACHE provides good predictions of the averaged and maximum concentrations.
- It performs within acceptable bounds of statistical measures for air quality applications.

Assessment of air quality in complex industrial and urban regions is essential in setting effective policies for sustainable development of society and assisting to improve the environment and thereby support health and safety. In this study, a CFD model fluidyn-PANACHE, dedicated to the dispersion of hazardous gases in complex industrial and urban sites, is critically validated with tracer observations from EMU experiment for two distinct types of sources in two geometrically different environments: (i) Case-A1 (release from an open door in courtyard area of a simple L-shaped building), and (ii) Case-C1 (continuous release over the larger distances around an industrial site featuring numerous buildings and complex local topography). The steady RANS solution is used first to get quick results and comparison with the experimental data shows a good agreement between the modelled and measured concentrations. Nevertheless, in order to examine the performance of the solver, unsteady RANS simulations are performed. A detailed statistical analysis shows that the performance of fluidyn-PANACHE against both Cases of EMU observations is well within the acceptable bounds of statistical measures for air quality applications. It is observed that in a simple geometry with lesser complexity in Case-A1, steady and unsteady simulations give similar performance against the tracer observations and predicts ≈68% points within a factor of two. In a more complex industrial site in Case-C1, unsteady RANS is performing slightly better than the steady simulation and it predicts ≈80.0% points within factor of two, which is higher than ≈67.0% points from the steady simulation.