GitLab

- if desired to create a tar-file without a _patch value in its name.

- add a '-SPDP' option

- remove the '-archOption' for forcing a '-m32' build on 64-bit
  architecture, which is now considered too obscure.
  Must edit files manually if this option is really required.

Digital-Filter Based Synthetic Turbulence Generation Method for LES/DES Inflow

See merge request !235

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:

./Allrun

Parallel (decompositionMethod=scotch) executing:

./Allrunparallel

    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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Integration adjoint

See merge request !269

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