rather than being calculated on construction and stored as member data.
The convergence warning has be replaced with the 'convergence()' member
function which returns 'true' if the SVD iteration converged, otherwise 'false'.
Provides efficient integration of complex laminar reaction chemistry,
combining the advantages of automatic dynamic specie and reaction
reduction with ISAT (in situ adaptive tabulation). The advantages grow
as the complexity of the chemistry increases.
References:
Contino, F., Jeanmart, H., Lucchini, T., & D’Errico, G. (2011).
Coupling of in situ adaptive tabulation and dynamic adaptive chemistry:
An effective method for solving combustion in engine simulations.
Proceedings of the Combustion Institute, 33(2), 3057-3064.
Contino, F., Lucchini, T., D'Errico, G., Duynslaegher, C.,
Dias, V., & Jeanmart, H. (2012).
Simulations of advanced combustion modes using detailed chemistry
combined with tabulation and mechanism reduction techniques.
SAE International Journal of Engines,
5(2012-01-0145), 185-196.
Contino, F., Foucher, F., Dagaut, P., Lucchini, T., D’Errico, G., &
Mounaïm-Rousselle, C. (2013).
Experimental and numerical analysis of nitric oxide effect on the
ignition of iso-octane in a single cylinder HCCI engine.
Combustion and Flame, 160(8), 1476-1483.
Contino, F., Masurier, J. B., Foucher, F., Lucchini, T., D’Errico, G., &
Dagaut, P. (2014).
CFD simulations using the TDAC method to model iso-octane combustion
for a large range of ozone seeding and temperature conditions
in a single cylinder HCCI engine.
Fuel, 137, 179-184.
Two tutorial cases are currently provided:
+ tutorials/combustion/chemFoam/ic8h18_TDAC
+ tutorials/combustion/reactingFoam/laminar/counterFlowFlame2D_GRI_TDAC
the first of which clearly demonstrates the advantage of dynamic
adaptive chemistry providing ~10x speedup,
the second demonstrates ISAT on the modest complex GRI mechanisms for
methane combustion, providing a speedup of ~4x.
More tutorials demonstrating TDAC on more complex mechanisms and cases
will be provided soon in addition to documentation for the operation and
settings of TDAC. Also further updates to the TDAC code to improve
consistency and integration with the rest of OpenFOAM and further
optimize operation can be expected.
Original code providing all algorithms for chemistry reduction and
tabulation contributed by Francesco Contino, Tommaso Lucchini, Gianluca
D’Errico, Hervé Jeanmart, Nicolas Bourgeois and Stéphane Backaert.
Implementation updated, optimized and integrated into OpenFOAM-dev by
Henry G. Weller, CFD Direct Ltd with the help of Francesco Contino.
Note: this reuses the existing storage rather than costly reallocation
which requires the initial allocation to be sufficient for the largest
size the ODE system might have. Attempt to set a size larger than the
initial size is a fatal error.
e.g. to avoid excessive unphysical velocities generated during slamming events in
incompressible VoF simulations
Usage
Example usage:
limitU
{
type limitVelocity;
active yes;
limitVelocityCoeffs
{
selectionMode all;
max 100;
}
}
Contributed by Alberto Passalacqua, Iowa State University
Foam::dragModels::Beetstra
Drag model of Beetstra et al. for monodisperse gas-particle flows obtained
with direct numerical simulations with the Lattice-Boltzmann method and
accounting for the effect of particle ensembles.
Reference:
\verbatim
Beetstra, R., van der Hoef, M. A., & Kuipers, J. a. M. (2007).
Drag force of intermediate Reynolds number flow past mono- and
bidisperse arrays of spheres.
AIChE Journal, 53(2), 489–501.
\endverbatim
Foam::dragModels::Tenneti
Drag model of Tenneti et al. for monodisperse gas-particle flows obtained
with particle-resolved direct numerical simulations and accounting for the
effect of particle ensembles.
Reference:
\verbatim
Tenneti, S., Garg, R., & Subramaniam, S. (2011).
Drag law for monodisperse gas–solid systems using particle-resolved
direct numerical simulation of flow past fixed assemblies of spheres.
International Journal of Multiphase Flow, 37(9), 1072–1092.
\verbatim
