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@ARTICLE{Rttgers:904832,
      author       = {Rüttgers, Mario and Waldmann, Moritz and Schröder,
                      Wolfgang and Lintermann, Andreas},
      title        = {{A} machine-learning-based method for automatizing
                      lattice-{B}oltzmann simulations of respiratory flows},
      journal      = {Applied intelligence},
      volume       = {52},
      issn         = {0924-669X},
      publisher    = {Springer Science + Business Media B.V},
      reportid     = {FZJ-2022-00157},
      pages        = {9080–9100},
      year         = {2022},
      abstract     = {Many simulation workflows require to prepare the data for
                      the simulation manually. This is time consuming and leads to
                      a massive bottleneck when a large number of numerical
                      simulations is requested. This bottleneck can be overcome by
                      an automated data processing pipeline. Such a novel pipeline
                      is developed for a medical use case from rhinology, where
                      computer tomography recordings are used as input and flow
                      simulation data define the results. Convolutional neural
                      networks are applied to segment the upper airways and to
                      detect and prepare the in- and outflow regions for accurate
                      boundary condition prescription in the simulation. The
                      automated process is tested on three cases which have not
                      been used to train the networks. The accuracy of the
                      pipeline is evaluated by comparing the network-generated
                      output surfaces to those obtained from a semi-automated
                      procedure performed by a medical professional. Except for
                      minor deviations at interfaces between ethmoidal sinuses,
                      the network-generated surface is sufficiently accurate. To
                      further analyze the accuracy of the automated pipeline, flow
                      simulations are conducted with a thermal lattice-Boltzmann
                      method for both cases on a high- performace computing
                      system. The comparison of the results of the respiratory
                      flow simulations yield averaged errors of less than $1\%$
                      for the pressure loss between the in- and outlets, and for
                      the outlet temperature. Thus, the pipeline is shown to work
                      accurately and the geometrical deviations at the ethmoidal
                      sinuses to be negligible.},
      cin          = {JSC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / HDS LEE - Helmholtz
                      School for Data Science in Life, Earth and Energy (HDS LEE)
                      (HDS-LEE-20190612)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel1)HDS-LEE-20190612},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000739280000001},
      doi          = {10.1007/s10489-021-02808-2},
      url          = {https://juser.fz-juelich.de/record/904832},
}