TY  - JOUR
AU  - Bode, Mathis
AU  - Gauding, Michael
AU  - Goeb, Dominik
AU  - Falkenstein, Tobias
AU  - Pitsch, Heinz
TI  - Applying physics-informed enhanced super-resolution generative adversarial networks to turbulent premixed combustion and engine-like flame kernel direct numerical simulation data
JO  - Proceedings of the Combustion Institute
VL  - 39
IS  - 4
SN  - 1540-7489
CY  - New York, NY [u.a.]
PB  - Elsevier ScienceDirect
M1  - FZJ-2024-00885
SP  -  5289-5298
PY  - 2023
AB  - Models for finite-rate-chemistry in underresolved flows still pose one of the main challenges for predictive simulations of complex configurations. The problem gets even more challenging if turbulence is involved. This work advances the recently developed PIESRGAN modeling approach to turbulent premixed combustion. For that, the physical information processed by the network and considered in the loss function are adjusted, the training process is smoothed, and especially effects from density changes are considered. The resulting model provides good results for a priori and a posteriori tests on direct numerical simulation data of a fully turbulent premixed flame kernel. The limits of the modeling approach are discussed. Finally, the model is employed to compute further realizations of the premixed flame kernel, which are analyzed with a scale-sensitive framework regarding their cycle-to-cycle variations. The work shows that the data-driven PIESRGAN subfilter model can very accurately reproduce direct numerical simulation data on much coarser meshes, which is hardly possible with classical subfilter models, and enables studying statistical processes more efficiently due to the smaller computing cost.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001012150200001
DO  - DOI:10.1016/j.proci.2022.07.254
UR  - https://juser.fz-juelich.de/record/1021622
ER  -