Abstract
The porous media approach has become more popular thus, it solves the equations of motion and energy numerically and therefore obtains detailed distributions of temperature and airspeed. However, those models are not allowed to forecast the relationships between the porosity of the volume of the crop with respect to the variables that comprise the climate in natural ventilation greenhouses at the same time in terms of probability. A porous media model of the crop and its approximations were developed and analyzed through non-supervised Bayesian Networks clustering, with the aim of determining the influence of porous media in function to the density crop, over the climate conditions in a natural ventilation greenhouse. Also, a naïve Bayes model unsupervised by the EM algorithm, initialized with random parameters was developed. The resulting model maximized the likelihood of the training data set. The relationships between the pressure drops in the flow limits at the crop were established. Porosity is directly influenced by humidity, temperature and slowly to CO2 concentration. Solar radiation, speed air and slowly the height are inversely influenced with the porosity. Naïve Bayes EM application to a CFD model has been providing a greater understanding of the interactions between the variables.
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