Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predictive data-driven model based on generative adversarial network for
  premixed turbulence-combustion regimes   Premixed flames exhibit different asymptotic regimes of interaction between
heat release and turbulence depending on their respective length scales. At
high Karlovitz number, the dilatation caused by heat release does not have any
relevant effect on turbulent kinetic energy with respect to non-reacting flow,
while at low Karlovitz number, the mean shear is a sink of turbulent kinetic
energy, and counter-gradient transport is observed. This latter phenomenon is
not well captured by closure models commonly used in Large Eddy Simulations
that are based on gradient diffusion. The massive amount of data available from
Direct Numerical Simulation (DNS) opens the possibility to develop data-driven
models able to represent physical mechanisms and non-linear features present in
both these regimes. In this work, the databases are formed by DNSs of two
planar hydrogen/air flames at different Karlovitz numbers corresponding to the
two asymptotic regimes. In this context, the Generative Adversarial Network
(GAN) gives the possibility to successfully recognize and reconstruct both
gradient and counter-gradient phenomena if trained with databases where both
regimes are included. Two GAN models were first trained each for a specific
Karlovitz number and tested using the same dataset in order to verify the
capability of the models to learn the features of a single asymptotic regime
and assess its accuracy. In both cases, the GAN models were able to reconstruct
the Reynolds stress subfilter scales accurately. Later, the GAN was trained
with a mixture of both datasets to create a model containing physical knowledge
of both combustion regimes. This model was able to reconstruct the subfilter
scales for both cases capturing the interaction between heat release and
turbulence closely to the DNS as shown from the turbulent kinetic budget and
barycentric maps.

16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
1
The categories of this paper are: cs
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc