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@ARTICLE{Hassanian:1024403,
      author       = {Hassanian, Reza and Aach, Marcel and Lintermann, Andreas
                      and Helgadóttir, Ásdís and Riedel, Morris},
      title        = {{T}urbulent {F}low {P}rediction-{S}imulation: {S}trained
                      {F}low with {I}nitial {I}sotropic {C}ondition {U}sing a
                      {GRU} {M}odel {T}rained by an {E}xperimental {L}agrangian
                      {F}ramework, with {E}mphasis on {H}yperparameter
                      {O}ptimization},
      journal      = {Fluids},
      volume       = {9},
      number       = {4},
      issn         = {2311-5521},
      address      = {Belgrade},
      publisher    = {MDPI},
      reportid     = {FZJ-2024-02146},
      pages        = {84},
      year         = {2024},
      note         = {The paper is available open-access on the publisher
                      website.},
      abstract     = {This study presents a novel approach to using a gated
                      recurrent unit (GRU) model, a deep neural network, to
                      predict turbulent flows in a Lagrangian framework. The
                      emerging velocity field is predicted based on experimental
                      data from a strained turbulent flow, which was initially a
                      nearly homogeneous isotropic turbulent flow at the
                      measurement area. The distorted turbulent flow has a Taylor
                      microscale Reynolds number in the range of 100 < $Re_\lamda$
                      < 152 before creating the strain and is strained with a mean
                      strain rate of 4 $s^−1$ in the Y direction. The
                      measurement is conducted in the presence of gravity
                      consequent to the actual condition, an effect that is
                      usually neglected and has not been investigated in most
                      numerical studies. A Lagrangian particle tracking technique
                      is used to extract the flow characterizations. It is used to
                      assess the capability of the GRU model to forecast the
                      unknown turbulent flow pattern affected by distortion and
                      gravity using spatiotemporal input data. Using the flow
                      track’s location (spatial) and time (temporal) highlights
                      the model’s superiority. The suggested approach provides
                      the possibility to predict the emerging pattern of the
                      strained turbulent flow properties observed in many natural
                      and artificial phenomena. In order to optimize the consumed
                      computing, hyperparameter optimization (HPO) is used to
                      improve the GRU model performance by $14–20\%.$ Model
                      training and inference run on the high-performance computing
                      (HPC) JUWELS-BOOSTER and DEEP-DAM systems at the Jülich
                      Supercomputing Centre, and the code speed-up on these
                      machines is measured. The proposed model produces accurate
                      predictions for turbulent flows in the Lagrangian view with
                      a mean absolute error (MAE) of 0.001 and an $R^2$ score of
                      0.993.},
      cin          = {JSC},
      ddc          = {530},
      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) /
                      EUROCC-2 (DEA02266)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)951733 /
                      G:(DE-Juel-1)DEA02266},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001211140400001},
      doi          = {10.3390/fluids9040084},
      url          = {https://juser.fz-juelich.de/record/1024403},
}