1. 26 Jun, 2019 1 commit
  2. 21 Jun, 2019 1 commit
    • Kutalmis Bercin's avatar
      ENH: Digital-Filter Based Synthetic Turbulence Generation Method for LES/DES Inflows · d6aac2ac
      Kutalmis Bercin authored
          Velocity boundary condition generating synthetic turbulence-alike
          time-series for LES and DES turbulent flow computations.
      
          To this end, two synthetic turbulence generators can be chosen:
          - Digital-filter method-based generator (DFM)
      
          \verbatim
          Klein, M., Sadiki, A., and Janicka, J.
              A digital filter based generation of inflow data for spatially
              developing direct numerical or large eddy simulations,
              Journal of Computational Physics (2003) 186(2):652-665.
              doi:10.1016/S0021-9991(03)00090-1
          \endverbatim
      
          - Forward-stepwise method-based generator (FSM)
      
          \verbatim
          Xie, Z.-T., and Castro, I.
              Efficient generation of inflow conditions for large eddy simulation of
              street-scale flows, Flow, Turbulence and Combustion (2008) 81(3):449-470
              doi:10.1007/s10494-008-9151-5
          \endverbatim
      
          In DFM or FSM, a random number set (mostly white noise), and a group
          of target statistics (mostly mean flow, Reynolds stress tensor profiles and
          length-scale sets) are fused into a new number set (stochastic time-series,
          yet consisting of the statistics) by a chain of mathematical operations
          whose characteristics are designated by the target statistics, so that the
          realised statistics of the new sets could match the target.
      
          Random number sets ---->-|
                                   |
                               DFM or FSM ---> New stochastic time-series consisting
                                   |           turbulence statistics
          Turbulence statistics ->-|
      
          The main difference between DFM and FSM is that the latter replaces the
          streamwise convolution summation in DFM by a simpler and a quantitatively
          justified equivalent procedure in order to reduce computational costs.
          Accordingly, the latter potentially brings resource advantages for
          computations involving relatively large length-scale sets and small
          time-steps.
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