Modelling the dispersion of aerial pollutants from agricultural buildings — an evaluation of computational fluid dynamics (CFD)

• Published in: Computers and electronics in agriculture Vol. 30; no. 1; pp. 219 - 235
• Main Authors: Quinn, A.D, Wilson, M, Reynolds, A.M, Couling, S.B, Hoxey, R.P
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2001
• Subjects: Atmosphere; Computational fluid dynamics; Dispersion modelling; Environment; Pollution; Environment; Pollution; Computational fluid dynamics; Dispersion modelling; Atmosphere
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/S0168-1699(00)00166-6

Agricultural buildings, especially those used for intensive livestock production, are significant sources of aerial pollutants. This paper explores some of the computational modelling methods available to predict near field concentrations of a pollutant emitted from a building opening. The modelling approaches used here are restricted in application to effectively weightless particles, such as a low concentration pollutant gas. The flow patterns were predicted using computational fluid dynamics (CFD) and linked to this computed flow field were two dispersion models, a Eulerian diffusion model and a Lagrangian particle tracking technique, both used to predict ensemble mean gas concentration. Explicit account has been taken here of variations in mean wind direction, using a new technique based on the weighted summation of individual wind direction results according to the probability density function of the wind direction. The atmospheric boundary layer is characterised by variations in wind direction that do not occur in simulated wind tunnel flows and are not generally reproduced in current computational methods. For comparison, the results of the modelling approach are compared with mean concentrations of ammonia gas released as a tracer from an isolated low rise building. The results of this comparison indicate that at a distance of more than three building heights downstream the predictions from both models are satisfactory but that in the near wake the diffusion model is less successful. The weighted solutions, taking account of wind direction, give significantly improved predictions over unweighted results. Lack of plume spread is identified as the main cause of inaccuracies in predictions and this is linked to inadequate resolution of flow features and mixing in the CFD model. The use of modelling based on CFD and existing dispersion models is clearly limited and further work on non-steady state simulations of wake flows for dispersion studies is required.