1. 08 Jul, 2019 1 commit
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  7. 24 Jun, 2019 10 commits
  8. 21 Jun, 2019 5 commits
    • Andrew Heather's avatar
      Merge branch 'feature-digitalFilterFvPatchInlet' into 'develop' · 9370053b
      Andrew Heather authored
      Digital-Filter Based Synthetic Turbulence Generation Method for LES/DES Inflow
      See merge request !235
    • Kutalmış Berçin's avatar
      TUT: add new tutorial case for turbulent inflow BCs · a31a0103
      Kutalmış Berçin authored
      The following three synthetic turbulence inflow boundary conditions are
      examined through single-cell-domain smooth-wall plane channel flow setup:
      - turbulentDFSEMInlet
      - turbulentDigitalFilterInlet variant=digitalFilter
      - turbulentDigitalFilterInlet variant=reducedDigitalFilter
      The examinations are performed in terms of the first-/second-order turbulence
      statistics provided by (Moser et al., (1999)) doi.org/10.1063/1.869966
      from smooth-wall plane channel flow direct numerical simulations at Re=395.
      Serial executing:
      Parallel (decompositionMethod=scotch) executing:
    • Kutalmış Berçin's avatar
      ENH: Digital-Filter Based Synthetic Turbulence Generation Method for LES/DES Inflows · d6aac2ac
      Kutalmış Berçin 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)
          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.
          - Forward-stepwise method-based generator (FSM)
          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
          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
    • Kutalmış Berçin's avatar
    • Andrew Heather's avatar
      TUT: Tuorial updates · 0d72096e
      Andrew Heather authored
  9. 20 Jun, 2019 3 commits