Evaluation of various turbulence models to predict indoor conditions in a naturally ventilated room

In recent years, Computational Fluid Dynamics (CFD) has become a popular tool in building simulation. However, developing reliable CFD models requires a high level of expertise in fluid dynamics and numerical techniques. Furthermore, choosing the right turbulence model is a crucial issue for an accurate CFD analysis. The objective of this work is to utilise Reynolds Averaged Navier - Stokes (RANS) models to predict airflow patterns and air temperature stratification inside an operating naturally ventilated study room, occupied by a person working on a laptop. The paper is a continuation of a recently published work on CFD model calibration; and explores the performance of various turbulence models to accurately simulate indoor conditions. This is done through a comparison of the simulation results with the measurements in a normally operating building. The results of zero equation, standard k- #949;, RNG k- #949;, k- #949; EARSM, standard k- #969; and SST k- #969; turbulence models are qualitatively analysed and quantitatively evaluated against field measurements performed in a normally operating building. Based on the accuracy and computational stability of the simulations, recommendations are given for the most accurate turbulence model in predicting indoor conditions in a normally operating naturally ventilated room occupied by a person.