CFD prediction of the daytime climate evolution inside a greenhouse taking account of the crop interaction, sun path and ground conduction

Abstract

The management of the local climate inside a greenhouse is essential to control the plant growth and reach the expected quality level for commercialization. In recent years, Computational Fluid Dynamics (CFD) proved to be an efficient tool to predict the greenhouse climate and therefore could serve as decision-making tool in the next future. Nevertheless, up until now, very few studies included together the sun path, plant interaction with local environment, and ground inertia effects. In addition, validation was generally based on only one or two parameters. The present study aims at analysing the evolution of the climate inside a greenhouse using CFD and taking account of the main heat and mass transfer processes at a daily time scale. A particular attention was paid to the validation of the model, considering air temperature and humidity inside and above the crop, as well as leaf temperature, crop transpiration and ground temperature. The CFD model solved the Reynolds Averaged Navier-Stokes equations, using a k-e closure for turbulence. Specific submodels were established to include the sun path as well as the crop interaction with the local climate. The ground was also meshed to assess its inertia effects. 2D unsteady simulations were launched, considering only cases for which the wind blew perpendicular to the ridge. The validation of the model was undertaken on the basis of experiments conducted for a 100 m2 Venlo-greenhouse with Impatiens pot plants grown on shelves. A typical sunny day of spring in Angers, France, was retained. The model showed its ability to correctly predict the temperature and humidity dynamics inside the greenhouse as well as the leaf temperature and crop transpiration evolutions. Although slight discrepancies were observed for the air temperature and humidity inside the crop, the main trends were correctly predicted.