Evaluating the influence of turbulence models used in computational fluid dynamics for the prediction of airflows inside poultry houses

There are various turbulence models in the computational fluid dynamics (CFD) literature but none has so far proven to be universally applicable. Accurate simulations require the proper choice of model appropriate for each particular situation. In this study, the performance of three types of k-c turbulence model, the standard k-epsilon, renormalisation group (RNG) k-epsilon, and realisable k-epsilon, were evaluated for their ability to accurately simulate the internal turbulent flow of a poultry house. Each model's accuracy was analysed by comparing predicted and experimental results, and its performance was assessed using the coefficient of determination (r(2)), the root mean square error to the standard deviation ratio (RSR), and a Taylor diagram, which provides a concise statistical summary of how well the correlation (r) and standard deviation (SD) patterns match. The RSR values obtained for air temperature and airspeed were 0.57 and 0.19, 0.30 and 0.16, and 0.64 and 0.23 for the standard k-epsilon, RNG k-epsilon, and Realizable k-epsilon models, respectively, and showed that the RNG k-epsilon model predicted the airspeed and air temperature best. Other models also provided good results, particularly in predicting airspeed; however, their air temperature predictions were not as accurate as those of the RNG k-epsilon model. The results showed that RNG k-epsilon presented the best results overall, whilst realisable k-epsilon did not meet with our expectations. (C) 2019 IAgrE. Published by Elsevier Ltd. All rights reserved.