学術雑誌論文 Cholesky decomposition-based generation of artificial inflow turbulence including scalar fluctuation

大風, 翼  ,  Okaze, Tsubasa  ,  Mochida, A  ,  Mochida, A

159pp.23 - 32 , 2017-12
内容記述
a b s t r a c t This paper proposes a new method for generating turbulent fluctuations in wind velocity and scalars, such as temperature and contaminant concentration, based on a Cholesky decomposition of the time-averaged turbulent flux tensors of the momentum and the scalar for inflow boundary condition of large-eddy simulation (LES). The artificial turbulent fluctuations generated by this method satisfy not only the prescribed profiles for the turbulent fluxes of the momentum and the scalar but also the prescribed spatial and time correlations. Based on an existing method that is able to impose the spatial and time correlations using digital filters, random two-dimensional data are filtered to generate a set of two-dimensional data with the prescribed spatial correlation. Then, these data are combined with those from the previous time step by using two weighting factors based on an exponential function. The method was validated by applying generated inflow turbulence to an LES computation of contaminant dispersion in a half-channel flow.
a b s t r a c t This paper proposes a new method for generating turbulent fluctuations in wind velocity and scalars, such as temperature and contaminant concentration, based on a Cholesky decomposition of the time-averaged turbulent flux tensors of the momentum and the scalar for inflow boundary condition of large-eddy simulation (LES). The artificial turbulent fluctuations generated by this method satisfy not only the prescribed profiles for the turbulent fluxes of the momentum and the scalar but also the prescribed spatial and time correlations. Based on an existing method that is able to impose the spatial and time correlations using digital filters, random two-dimensional data are filtered to generate a set of two-dimensional data with the prescribed spatial correlation. Then, these data are combined with those from the previous time step by using two weighting factors based on an exponential function. The method was validated by applying generated inflow turbulence to an LES computation of contaminant dispersion in a half-channel flow.

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