Numerical predictions of indoor climate in large industrial premises. A comparison between different k–ε models supported by field measurements

This paper explores the benefits of using computational fluid dynamics (CFD) as a tool for prediction of indoor environment in large and complex industrial premises, in this case a packaging facility. This paper also presents a comparison between three eddy-viscosity turbulence models, i.e. the standard k–ε, the RNG k–ε, and the realizable k–ε, used for predictions of the flow pattern and temperature distribution in this large industrial facility. The predictions are compared with field measurements and the RNG k–ε model has been found to be the one most concurrent with the measured values. The paper also shows that a 50% reduction in the supply airflow rate can be an efficient energy efficiency measure, for the studied packaging facility, without compromising either product safety or thermal comfort. When implementing this efficiency measure it is predicted to lead to a reduction of the use of electricity and district heating by 85%, respectively, 61%. The energy use is calculated using IDA ICE 3.0. The ventilation effectiveness for heat removal (εt) and percentage dissatisfied (PD-index) are used to evaluate the indoor climate.