Commit 33ef139a authored by Committed by Andrew HeatherBrowse files
ENH: Digital-Filter Based Synthetic Turbulence Generation Method for LES/DES Inflows
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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