1. 21 Aug, 2019 1 commit
  2. 19 Aug, 2019 1 commit
  3. 14 Aug, 2019 1 commit
  4. 13 Aug, 2019 1 commit
    • mattijs's avatar
      BUG: edge sync fails with cyclic baffles (fixes #1397) · 0fbfb59f
      mattijs authored
      - synchronization, reduction only makes sense on processor-coupled
        patches. Since cyclic baffles are within a single processor domain,
        they are not reduced. So need to skip the sanity test for these.
  5. 07 Aug, 2019 1 commit
  6. 06 Aug, 2019 1 commit
  7. 30 Jul, 2019 1 commit
  8. 24 Jul, 2019 1 commit
  9. 22 Jul, 2019 1 commit
  10. 12 Jul, 2019 2 commits
  11. 09 Jul, 2019 4 commits
  12. 08 Jul, 2019 3 commits
  13. 04 Jul, 2019 1 commit
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  15. 27 Jun, 2019 1 commit
  16. 26 Jun, 2019 6 commits
  17. 24 Jun, 2019 3 commits
  18. 21 Jun, 2019 2 commits
    • 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)
          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
    • Kutalmis Bercin's avatar
  19. 20 Jun, 2019 4 commits
  20. 19 Jun, 2019 4 commits
    • Vaggelis Papoutsis's avatar
      CONTRIB: New adjoint optimisation and tools · 32b7d7c2
      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
    • Andrew Heather's avatar
      STYLE: Updated FO output · 51910c6c
      Andrew Heather authored
    • Andrew Heather's avatar
    • mattijs's avatar
      ENH: scotch: make repeatable. Fixes #1274. · 45b0aed8
      mattijs authored