% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Bode:1021622,
      author       = {Bode, Mathis and Gauding, Michael and Goeb, Dominik and
                      Falkenstein, Tobias and Pitsch, Heinz},
      title        = {{A}pplying physics-informed enhanced super-resolution
                      generative adversarial networks to turbulent premixed
                      combustion and engine-like flame kernel direct numerical
                      simulation data},
      journal      = {Proceedings of the Combustion Institute},
      volume       = {39},
      number       = {4},
      issn         = {1540-7489},
      address      = {New York, NY [u.a.]},
      publisher    = {Elsevier ScienceDirect},
      reportid     = {FZJ-2024-00885},
      pages        = {5289-5298},
      year         = {2023},
      abstract     = {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.},
      cin          = {JSC},
      ddc          = {660},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / CoEC - Center of Excellence
                      in Combustion (952181)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(EU-Grant)952181},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001012150200001},
      doi          = {10.1016/j.proci.2022.07.254},
      url          = {https://juser.fz-juelich.de/record/1021622},
}