- 26 Jun, 2019 3 commits
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OpenFOAM bot authored
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Mark OLESEN authored
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Mark OLESEN authored
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- 25 Jun, 2019 7 commits
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Mark OLESEN authored
- if desired to create a tar-file without a _patch value in its name.
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sergio authored
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sergio authored
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Mark OLESEN authored
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Andrew Heather authored
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Mark OLESEN authored
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Mark OLESEN authored
- 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.
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- 24 Jun, 2019 10 commits
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Mark OLESEN authored
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Mark OLESEN authored
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sergio authored
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sergio authored
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Mark OLESEN authored
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Mark OLESEN authored
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Mark OLESEN authored
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Andrew Heather authored
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Andrew Heather authored
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Andrew Heather authored
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- 21 Jun, 2019 5 commits
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Andrew Heather authored
Digital-Filter Based Synthetic Turbulence Generation Method for LES/DES Inflow See merge request !235
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Kutalmış Berçin authored
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
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Kutalmış Berçin authored
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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Kutalmış Berçin authored
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Andrew Heather authored
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- 20 Jun, 2019 5 commits
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mattijs authored
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Andrew Heather authored
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mattijs authored
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mattijs authored
Please enter the commit message for your changes. Lines starting
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mattijs authored
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- 19 Jun, 2019 8 commits
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Andrew Heather authored
Integration adjoint See merge request !269
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Vaggelis Papoutsis authored
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
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Andrew Heather authored
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Andrew Heather authored
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mattijs authored
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Andrew Heather authored
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Andrew Heather authored
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Andrew Heather authored
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- 18 Jun, 2019 2 commits
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Andrew Heather authored
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Andrew Heather authored
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