• Title/Summary/Keyword: Elliptic-blending model

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ANALYSIS OF RAYLEIGH-BENARD NATURAL CONVECTION WITH THE SECOND-MOMENT TURBULENCE MODEL (이차모멘트 난류모델을 사용한 Rayleigh-Benard 자연대류 유동 해석)

  • Choi, Seok-Ki;Kim, Seong-O
    • 한국전산유체공학회:학술대회논문집
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    • 2008.03a
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    • pp.111-117
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    • 2008
  • This paper reports briefly on the computational results of a turbulent Rayleigh-Benard convection with the elliptic-blending second-moment closure (EBM). The primary emphasis of the study is placed on an investigation of accuracy and numerical stability of the elliptic-blending second-moment closure for the turbulent Rayleigh-Benard convection. The turbulent heat fluxes in this study are treated by the algebraic flux model with the temperature variance and molecular dissipation rate of turbulent heat flux. The model is applied to the prediction of the turbulent Rayleigh-Benard convection for Rayleigh numbers ranging from $Ra=2{\times}10^6$ to $Ra=10^9$, and the computed results are compared with the previous experimental correlations, T-RANS and LES results. The predicted cell-averaged Nusselt number follows the correlation by Peng et al.(2006) ($Nu=0.162Ra^{0.286}$) in the 'soft' convective turbulence region ($2{\times}10^6{\leq}Ra{\leq}4{\times}10^7$) and it follows the experimental correlation by Niemela et al. (2000) ($Nu=0.124Ra^{0.309}$) in the 'hard' convective tubulence region ($10^8{\leq}Ra{\leq}10^9$) within 5% accuracy. This results show that the elliptic-blending second-moment closure with an algebraic flux model predicts very accurately the Rayleigh Benard convection.

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ANALYSIS OF RAYLEIGH-BENARD NATURAL CONVECTION (Rayleigh-Benard 자연대류 유동 해석)

  • Choi, Seok-Ki;Kim, Seong-O
    • Journal of computational fluids engineering
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    • v.13 no.3
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    • pp.62-68
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    • 2008
  • This paper reports briefly on the computational results of a turbulent Rayleigh-Benard convection with the elliptic-blending second-moment closure (EBM). The primary emphasis of the study is placed on an investigation of accuracy and numerical stability of the elliptic-blending second-moment closure for the turbulent Rayleigh-Benard convection. The turbulent heat fluxes in this study are treated by the algebraic flux model with the temperature variance and molecular dissipation rate of turbulent heat flux. The model is applied to the prediction of the turbulent Rayleigh-Benard convection for Rayleigh numbers ranging from Ra=$2{\times}10^6$ to Ra=$10^9$ and the computed results are compared with the previous experimental correlations, T-RANS and LES results. The predicted cell-averaged Nusselt number follows the correlation by Peng et al.(2006) (Nu=$0.162Ra^{0.286}$) in the 'soft' convective turbulence region ($2{\times}10^6{\leq}Ra{\leq}4{\times}10^7$) and it follows the experimental correlation by Niemela et al. (2000) (N=$0.124Ra^{0.309}$) in the 'hard' convective turbulence region ($10^8{\leq}Ra{\leq}10^9$) within 5% accuracy. This results show that the elliptic-blending second-moment closure with an algebraic flux model predicts very accurately the Rayleigh-Benard convection.

ANALYSIS OF A STRATIFIED NATURAL CONVECTION FLOW WITH THE SECOND-MOMENT CLOSURE (이차모멘트 난류모델을 사용한 성층화된 자연대류 유동 해석)

  • Choi, Seok-Ki;Kim, Seong-O
    • Journal of computational fluids engineering
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    • v.12 no.3
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    • pp.55-61
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    • 2007
  • A computational study on a strongly stratified natural convection is performed with the elliptic blending second-moment closure. The turbulent heat flux is treated by both the algebraic flux model (AFM) and the differential flux model (DFM). Calculations are performed for a turbulent natural convection in a square cavity with conducting top and bottom walls and the calculated results are compared with the available experimental data. The results show that both the AFM and DFM models produce very accurate solutions with the elliptic-blending second-moment closure without invoking any numerical stability problems. These results show that the AFM and DFM models for treating the turbulent heat flux are sufficient for this strongly stratified flow. However, a slight difference between two models is observed for some variables.