% 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”.

@INPROCEEDINGS{Shao:1041830,
      author       = {Shao, Xiao and Ayan, Hilmi Oguzhan and Hübenthal, Fabian
                      and Rüttgers, Mario and Lintermann, Andreas and Schröder,
                      Wolfgang},
      title        = {{I}nvestigating the {E}ffects of {S}panwise {T}ransversal
                      {T}raveling {W}aves on a {T}urbulent {C}ompressible {F}lat
                      {P}late {F}low {W}ith the {A}id of a {D}eep {A}utoencoder
                      {N}etwork},
      volume       = {69},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2025-02462},
      series       = {Schriften des Forschungszentrums Jülich IAS Series},
      pages        = {89-93},
      year         = {2025},
      comment      = {Proceedings of the 35th Parallel CFD International
                      Conference 2024},
      booktitle     = {Proceedings of the 35th Parallel CFD
                       International Conference 2024},
      abstract     = {The impact of spanwise traveling transversal surface waves
                      on drag reduction in turbulent compressible flat plate flow
                      is explored. The findings indicate that when the traveling
                      phase speed approaches the freestream velocity at 𝑀 =
                      0.7, a shock wave is induced in the spanwise direction. This
                      shock wave effectively breaks down streamwise vortices into
                      smaller scales, which significantly enhances drag reduction.
                      The spanwise shock wave is a large-scale quasi-periodic
                      phenomenon. To understand its impact on the multi-scale
                      nature of turbulent flows, a nonlinear mode decomposing deep
                      convolutional autoencoder is employed. The results show that
                      the autoencoder reconstructs the flow field more accurately
                      compared to Proper Orthogonal Decomposition (POD) and
                      Dynamic Mode Decomposition(DMD). Additionally, it
                      effectively separates the large-scale spanwise shock wave
                      and small-scale turbulent structures, which achieves a
                      clearer distinction than POD and DMD.},
      month         = {Sep},
      date          = {2024-09-02},
      organization  = {35th Parallel CFD International
                       Conference 2024, Bonn (Germany), 2 Sep
                       2024 - 4 Sep 2024},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / RAISE - Research on
                      AI- and Simulation-Based Engineering at Exascale (951733)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)951733},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.34734/FZJ-2025-02462},
      url          = {https://juser.fz-juelich.de/record/1041830},
}