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@ARTICLE{Bode:1021623,
      author       = {Bode, Mathis and Göbbert, Jens Henrik},
      title        = {{A}cceleration of complex high-performance computing
                      ensemble simulations with super-resolution-based subfilter
                      models},
      journal      = {Computers $\&$ fluids},
      volume       = {271},
      issn         = {0045-7930},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2024-00886},
      pages        = {106150},
      year         = {2024},
      abstract     = {Direct numerical simulation (DNS) of fluid flow problems
                      has been one of the most important applications of
                      high-performance computing (HPC) in the last decades. For
                      example, turbulent flows require the simultaneous resolution
                      of multiple spatial and temporal scales as all scales are
                      coupled, resulting in very large simulations with enormous
                      degrees of freedom. Another example is reactive flows, which
                      typically result in a large system of coupled differential
                      equations and multiple transport equations that must be
                      solved simultaneously. In addition, many flows exhibit
                      chaotic behavior, meaning that only statistical ensembles of
                      results can be compared, further increasing the
                      computational time. In this work, a combined HPC/deep
                      learning (DL) workflow is presented that drastically reduces
                      the overall computational time required while still
                      providing acceptable accuracy.Traditionally, all the
                      simulations required to compute ensemble statistics are
                      performed using expensive DNS. The idea behind the combined
                      HPC/DL workflow is to reduce the number of expensive DNSs by
                      developing a DL-assisted large-eddy simulation (LES)
                      approach that uses a sophisticated DL network, called
                      PIESRGAN, as a subfilter model for all unclosed terms and is
                      accurate enough to substitute DNSs. The remaining DNSs are
                      thus used in two ways: first, as data contributing to the
                      ensemble statistics, and second, as data used to train the
                      DL network. It was found that in many cases two remaining
                      DNSs are sufficient for training the LES approach. The cost
                      of the DL-supported LES is usually more than one order of
                      magnitude cheaper than the DNS, which drastically speeds up
                      the workflow, even considering the overhead for training the
                      DL network.},
      cin          = {JSC},
      ddc          = {004},
      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:001164689600001},
      doi          = {10.1016/j.compfluid.2023.106150},
      url          = {https://juser.fz-juelich.de/record/1021623},
}