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@ARTICLE{Bode:917411,
      author       = {Bode, Mathis},
      title        = {{A}pplying {P}hysics-{I}nformed {E}nhanced
                      {S}uper-{R}esolution {G}enerative {A}dversarial {N}etworks
                      to {L}arge-{E}ddy {S}imulations of {ECN} {S}pray {C}},
      journal      = {SAE international journal of advances and current practices
                      in mobility},
      volume       = {4},
      number       = {6},
      issn         = {2641-9637},
      address      = {Warrendale, Pa.},
      publisher    = {Soc.},
      reportid     = {FZJ-2023-00625},
      pages        = {2211-2219},
      year         = {2022},
      abstract     = {Large-eddy simulation (LES) is an important tool to
                      understand and analyze sprays, such as those found in
                      engines. Subfilter models are crucial for the accuracy of
                      spray-LES, thereby signifying the importance of their
                      development for predictive spray-LES. Recently, new
                      subfilter models based on physics-informed generative
                      adversarial networks (GANs) were developed, known as
                      physics-informed enhanced super-resolution GANs (PIESRGANs).
                      These models were successfully applied to the Spray A case
                      defined by the Engine Combustion Network (ECN). This work
                      presents technical details of this novel method, which are
                      relevant for the modeling of spray combustion, and applies
                      PIESRGANs to the ECN Spray C case. The results are validated
                      against experimental data, and computational challenges and
                      advantages are particularly emphasized compared to classical
                      simulation approaches.},
      cin          = {JSC},
      ddc          = {620},
      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) / Using deep learning to predict
                      statistics of turbulent flows at high Reynolds numbers
                      $(jhpc55_20190501)$},
      pid          = {G:(DE-HGF)POF4-5112 / G:(EU-Grant)952181 /
                      $G:(DE-Juel1)jhpc55_20190501$},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.4271/2022-01-0503},
      url          = {https://juser.fz-juelich.de/record/917411},
}