GitLab

- the scotch interface still uses non-const pointers when passing in
  values. For the ConstPrecisionAdaptor this means that we need to cheat
  with a constCast(). Using ref() will rightly trigger complaints about
  trying to modify a const object.

BUG: DimensionedField - register new field if it does not have the same name as the copy - see #1348

TODO: need to revisit

- 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.