- changed for 1906 to have a regIOobject always cleanup its
objectRegistry entry. No need to explicitly 'release' ownership
for this to now happen.
- doxygen formatting for MeshObject
- used fallback of 0 instead of the results time.
This discrepancy caused the case file to have two timesets that
only differed by the first (incorrect) entry.
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.
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
- now only needed when specify compiling -m32 on a 64-bit system.
Internally use the __SIZEOF_LONG__ compiler macro (gcc, icc, llvm)
to define when long is actually an int32_t.
