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@ARTICLE{Froning:943321,
      author       = {Froning, Dieter and Wirtz, Jannik and Hoppe, Eugen and
                      Lehnert, Werner},
      title        = {{F}low {C}haracteristics of {F}ibrous {G}as {D}iffusion
                      {L}ayers {U}sing {M}achine {L}earning {M}ethods},
      journal      = {Applied Sciences},
      volume       = {12},
      number       = {23},
      issn         = {2076-3417},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2023-00927},
      pages        = {12193 -},
      year         = {2022},
      abstract     = {The material characteristics of gas diffusion layers are
                      relevant for the efficient operation of polymer electrolyte
                      fuel cells. The current state-of-the-art calculates these
                      using transport simulations based on their micro-structures,
                      either reconstructed or generated by means of stochastic
                      geometry models. Such transport simulations often require
                      high computational resources. To support material
                      characterization using artificial-intelligence-based
                      methods, in this study, a convolutional neural network was
                      developed. It was trained with results from previous
                      transport simulations and validated using five-fold
                      cross-validation. The neural network enables the
                      permeability of paper-type gas diffusion layers to be
                      predicted. A stochastic arrangement of the fibers, four
                      types of binder distributions, and compression of up to
                      $50\%$ are also considered. The binder type and compression
                      level were features inherent to the material but were not
                      the subject of the training. In this regard, they can be
                      seen as features hidden from the training process.
                      Nevertheless, these characteristics were reproduced with the
                      proposed machine learning model. With a trained machine
                      learning model, the prediction of permeability can be
                      performed on a standard computer.},
      cin          = {IEK-14},
      ddc          = {600},
      cid          = {I:(DE-Juel1)IEK-14-20191129},
      pnm          = {1231 - Electrochemistry for Hydrogen (POF4-123)},
      pid          = {G:(DE-HGF)POF4-1231},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000912433600001},
      doi          = {10.3390/app122312193},
      url          = {https://juser.fz-juelich.de/record/943321},
}