TY - JOUR
T1 - Cholesky decomposition–based generation of artificial inflow turbulence including scalar fluctuation
AU - Okaze, T.
AU - Mochida, A.
N1 - Funding Information:
The authors are grateful for the support provided by the Grant-in-Aid for Scientific Research (C) (Grant No. 26420578 ). This research was also supported by the joint research project of the Wind Engineering Joint Usage/Research Center at Tokyo Polytechnic University (grant number 173007 ).
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017/12/15
Y1 - 2017/12/15
N2 - 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.
AB - 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.
KW - Artificial turbulence generation
KW - Cholesky decomposition
KW - Inflow boundary condition
KW - Large-eddy simulation
KW - Space correlation
KW - Time correlation
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U2 - 10.1016/j.compfluid.2017.09.005
DO - 10.1016/j.compfluid.2017.09.005
M3 - Article
AN - SCOPUS:85029726956
SN - 0045-7930
VL - 159
SP - 23
EP - 32
JO - Computers and Fluids
JF - Computers and Fluids
ER -