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  1. Feb 18, 2019
    • Kutalmış Berçin's avatar
      ENH: Digital-Filter Based Synthetic Turbulence Generation Method for LES/DES Inflows · d5198a7f
      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)
      
          \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.
      d5198a7f
  2. Jun 14, 2019
  3. Jun 21, 2019
  4. Jun 20, 2019
  5. Jun 19, 2019
  6. Jun 18, 2019
  7. Jun 17, 2019
    • Vaggelis Papoutsis's avatar
      CONTRIB: New adjoint optimisation and tools · f4ef4d4b
      Vaggelis Papoutsis authored
      A set of libraries and executables creating a workflow for performing
      gradient-based optimisation loops. The main executable (adjointOptimisationFoam)
      solves the flow (primal) equations, followed by the adjoint equations and,
      eventually, the computation of sensitivity derivatives.
      
      Current functionality supports the solution of the adjoint equations for
      incompressible turbulent flows, including the adjoint to the Spalart-Allmaras
      turbulence model and the adjoint to the nutUSpaldingWallFunction, [1], [2].
      
      Sensitivity derivatives are computed with respect to the normal displacement of
      boundary wall nodes/faces (the so-called sensitivity maps) following the
      Enhanced Surface Integrals (E-SI) formulation, [3].
      
      The software was developed by PCOpt/NTUA and FOSS GP, with contributions from
      
      Dr. Evangelos Papoutsis-Kiachagias,
      Konstantinos Gkaragounis,
      Professor Kyriakos Giannakoglou,
      Andy Heather
      
      and contributions in earlier version from
      
      Dr. Ioannis Kavvadias,
      Dr. Alexandros Zymaris,
      Dr. Dimitrios Papadimitriou
      
      [1] A.S. Zymaris, D.I. Papadimitriou, K.C. Giannakoglou, and C. Othmer.
      Continuous adjoint approach to the Spalart-Allmaras turbulence model for
      incompressible flows. Computers & Fluids, 38(8):1528–1538, 2009.
      
      [2] E.M. Papoutsis-Kiachagias and K.C. Giannakoglou. Continuous adjoint methods
      for turbulent flows, applied to shape and topology optimization: Industrial
      applications. 23(2):255–299, 2016.
      
      [3] I.S. Kavvadias, E.M. Papoutsis-Kiachagias, and K.C. Giannakoglou. On the
      proper treatment of grid sensitivities in continuous adjoint methods for shape
      optimization. Journal of Computational Physics, 301:1–18, 2015.
      
      Integration into the official OpenFOAM release by OpenCFD
      f4ef4d4b
    • Andrew Heather's avatar
      COMP: Added option for clang 8.0.0 · 55277f51
      Andrew Heather authored
      55277f51
    • mattijs's avatar
      COMP: lduMatrix: fix solveScalar compilation · 94ce12a7
      mattijs authored
      94ce12a7
    • mattijs's avatar
    • sergio's avatar
    • sergio's avatar
    • mattijs's avatar
  8. Jun 14, 2019
  9. Jun 13, 2019