Comparative Evaluation of an Eulerian CFD and Gaussian Plume Models Based on Prairie Grass Dispersion Experiment

Abstract

Abstract A theoretical and statistical comparison of a three-dimensional computational fluid dynamics (CFD) model with two Gaussian plume models is proposed on the Prairie Grass data field experiment for neutral conditions, using both maximum arcwise concentrations and spatially paired observations. In theory, it is impossible to have the same near-source behavior with the Eulerian CFD code as with the Gaussian plume models. The former presents the inability to account for the dependence of the turbulent diffusivity to the distance from the source, contrary to plume models for which this dependence is fitted to observations. The study described herein looks at the practical implications of these theoretical differences by comparing the two different types of models on a flat-terrain case, a situation favoring Gaussian models. The results herein show that the Eulerian CFD model gives acceptable results, both for arc-maximum concentrations and spatially paired observations. Indeed, the statistical performances are above the criteria of “good performance” commonly defined in literature. In general, the results for Eulerian code fall between those of a Gaussian model that has been fitted using the Prairie Grass dataset and those of one fitted with different datasets.