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@INPROCEEDINGS{Rttgers:902415,
      author       = {Rüttgers, Mario and Waldmann, Moritz and Schröder,
                      Wolfgang and Lintermann, Andreas},
      title        = {{M}achine-{L}earning-{B}ased {C}ontrol of {P}erturbed and
                      {H}eated {C}hannel {F}lows},
      volume       = {12761},
      publisher    = {Springer},
      reportid     = {FZJ-2021-04237},
      isbn         = {978-3-030-90538-5 (print)},
      series       = {Lecture Notes in Computer Science},
      pages        = {7 - 22},
      year         = {2021},
      comment      = {High Performance Computing / Jagode, Heike (Editor)},
      booktitle     = {High Performance Computing / Jagode,
                       Heike (Editor)},
      abstract     = {A reinforcement learning algorithm is coupled to a thermal
                      lattice-Boltzmann method to control flow through a
                      two-dimensional heated channel narrowed by a bump. The
                      algorithm is allowed to change the disturbance factor of the
                      bump and receives feedback in terms of the pressure loss and
                      temperature increase between the inflow and outflow region
                      of the channel. It is trained to modify the bump such that
                      both fluid mechanical properties are rated equally
                      important. After a modification, a new simulation is
                      initialized using the modified geometry and the flow field
                      computed in the previous run. The thermal lattice-Boltzmann
                      method is validated for a fully developed isothermal channel
                      flow. After 265 simulations, the trained algorithm predicts
                      an averaged disturbance factor that deviates by less than
                      $1\%$ from the reference solution obtained from 3,400
                      numerical simulations using a parameter sweep over the
                      disturbance factor. The error is reduced to less than
                      $0.1\%$ after 1,450 simulations. A comparison of the
                      temperature, pres- sure, and streamwise velocity
                      distributions of the reference solution with the solution
                      after 1,450 simulations along the line of the maximum
                      velocity component in streamwise direction shows only
                      negligible differences. The presented method is hence a
                      valid method for avoiding expensive parameter space
                      explorations and promises to be effective in supporting
                      shape optimizations for more complex configurations, e.g.,
                      in finding optimal nasal cavity shapes.},
      month         = {Jun},
      date          = {2021-06-24},
      organization  = {ISC High Performance 2021, Frankfurt
                       (Germany), 24 Jun 2021 - 2 Jul 2021},
      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},
      UT           = {WOS:000763168300001},
      doi          = {10.1007/978-3-030-90539-2_1},
      url          = {https://juser.fz-juelich.de/record/902415},
}