mirror of
https://develop.openfoam.com/Development/openfoam.git
synced 2025-11-28 03:28:01 +00:00
33ef139ac1
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.