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.