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      SST k-ω 모델의 옥상 배출구에서 배출되는 악취물질의 근거리 확산 모의 적용성에 대한 연구

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      https://www.riss.kr/link?id=A101216465

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      This paper presents the performance of a CFD model for the near field dispersion of odor from rooftop emissions. The FLUENT Shear-Stress Transport (SST hereinafter) k-ω turbulence model was used to simulate odor dispersion from a rooftop odor vent. The results were compared with a wind tunnel experiment and the calculated results of ASHRAE 2003 and 2007. The FLUENT SST k-ω turbulence model provided good results for making reasonable predictions about the building rooftop surface normalized dilution. It was found that increasing the vent height (from 1 m to 7 m) reduces rooftop surface normalized dilution. ASHRAE 2003 and ASHRAE 2007 performance measures are generally not as good as FLUENT SST k-ω turbulence model performance measures, with larger MG (the geometric mean bias, VG (the geometric variance), NMSE (the normalized mean square error), FB (Fractional bias), and smaller FAC2 (the fraction of predictions within a factor of two of observations).
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      This paper presents the performance of a CFD model for the near field dispersion of odor from rooftop emissions. The FLUENT Shear-Stress Transport (SST hereinafter) k-ω turbulence model was used to simulate odor dispersion from a rooftop odor vent. T...

      This paper presents the performance of a CFD model for the near field dispersion of odor from rooftop emissions. The FLUENT Shear-Stress Transport (SST hereinafter) k-ω turbulence model was used to simulate odor dispersion from a rooftop odor vent. The results were compared with a wind tunnel experiment and the calculated results of ASHRAE 2003 and 2007. The FLUENT SST k-ω turbulence model provided good results for making reasonable predictions about the building rooftop surface normalized dilution. It was found that increasing the vent height (from 1 m to 7 m) reduces rooftop surface normalized dilution. ASHRAE 2003 and ASHRAE 2007 performance measures are generally not as good as FLUENT SST k-ω turbulence model performance measures, with larger MG (the geometric mean bias, VG (the geometric variance), NMSE (the normalized mean square error), FB (Fractional bias), and smaller FAC2 (the fraction of predictions within a factor of two of observations).

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