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@ARTICLE{Bode:873125,
      author       = {Bode, Mathis and Gauding, Michael and Lian, Zeyu and
                      Denker, Dominik and Davidovic, Marco and Kleinheinz,
                      Konstantin and Jitsev, Jenia and Pitsch, Heinz},
      title        = {{U}sing {P}hysics-{I}nformed {S}uper-{R}esolution
                      {G}enerative {A}dversarial {N}etworks for {S}ubgrid
                      {M}odeling in {T}urbulent {R}eactive {F}lows},
      reportid     = {FZJ-2020-00570},
      year         = {2019},
      note         = {Submitted to Combustion Symposium 2020},
      abstract     = {Turbulence is still one of the main challenges for
                      accurately predicting reactive flows. Therefore, the
                      development of new turbulence closures which can be applied
                      to combustion problems is essential. Data-driven modeling
                      has become very popular in many fields over the last years
                      as large, often extensively labeled, datasets became
                      available and training of large neural networks became
                      possible on GPUs speeding up the learning process
                      tremendously. However, the successful application of deep
                      neural networks in fluid dynamics, for example for subgrid
                      modeling in the context of large-eddy simulations (LESs), is
                      still challenging. Reasons for this are the large amount of
                      degrees of freedom in realistic flows, the high requirements
                      with respect to accuracy and error robustness, as well as
                      open questions, such as the generalization capability of
                      trained neural networks in such high-dimensional,
                      physics-constrained scenarios. This work presents a novel
                      subgrid modeling approach based on a generative adversarial
                      network (GAN), which is trained with unsupervised deep
                      learning (DL) using adversarial and physics-informed losses.
                      A two-step training method is used to improve the
                      generalization capability, especially extrapolation, of the
                      network. The novel approach gives good results in a priori
                      as well as a posteriori tests with decaying turbulence
                      including turbulent mixing. The applicability of the network
                      in complex combustion scenarios is furthermore discussed by
                      employing it to a reactive LES of the Spray A case defined
                      by the Engine Combustion Network (ECN).},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512) / Using deep learning to predict statistics of
                      turbulent flows at high Reynolds numbers $(jhpc55_20180501)$
                      / Direct Numerical Simulations of Fluid Turbulence at High
                      Reynolds Numbers $(sbda006_20151101)$},
      pid          = {G:(DE-HGF)POF3-512 / $G:(DE-Juel1)jhpc55_20180501$ /
                      $G:(DE-Juel1)sbda006_20151101$},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {1911.11380},
      howpublished = {arXiv:1911.11380},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:1911.11380;\%\%$},
      url          = {https://juser.fz-juelich.de/record/873125},
}