Evaluation of turbulence models to predict airflow and ammonia concentrations in a scale model swine building enclosure

Guohong Tong, Guoqiang Zhang, David M. Christopher, Bjarne Schmidt Bjerg, Zhangying Ye, Jin Cheng

    10 Citations (Scopus)

    Abstract

    The performance of five widely used turbulence models, the standard k-ε model (SKE), the renormalization group k-ε model (RNG), the realizable k-ε model (RKE), the standard k-ω model (SKW) and the shear stress transport k-ω model (KWSST), were evaluated for simulations of airflow velocities and ammonia concentrations in a 1:12.5 scale model swine building without a floor (100% floor opening) and with a slatted floor with 16.7% floor opening area. The 100% floor opening case was used as a reference. The turbulence models were evaluated by comparing the numerical results with experimental data at representative points inside the scale model. The RKE and RNG models required less elements for grid-independent results with the predicted airflow patterns agreeing well with the smoke tests. The velocities and concentrations predicted by the RNG model were closer to the measured values with a maximum velocity difference of less than 0.03ms-1 (9.3%) and a maximum normalized concentration difference of less than 0.09 (12.3%) for the 100% floor opening. For the 16.7% floor opening, the maximum velocity difference in the main space was less than 0.02ms-1 (6.8%) and the maximum normalized concentration difference was less than 0.2 (25%). Thus, the RNG model most accurately predicts the airflow velocities and ammonia concentrations in the scale model swine building enclosure.

    Original languageEnglish
    JournalComputers & Fluids
    Volume71
    Pages (from-to)240-249
    Number of pages10
    ISSN0045-7930
    DOIs
    Publication statusPublished - Jan 2013

    Fingerprint

    Dive into the research topics of 'Evaluation of turbulence models to predict airflow and ammonia concentrations in a scale model swine building enclosure'. Together they form a unique fingerprint.

    Cite this