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@PHDTHESIS{Wiesner:1028896,
      author       = {Wiesner, Florian},
      othercontributors = {Wessling, Matthias and Gallart, Marc Secanell},
      title        = {{T}wo-phase flow simulations in gas diffusion electrodes},
      volume       = {64},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {Aachener Verfahrenstechnik},
      reportid     = {RWTH-2026-01948},
      series       = {Aachener Verfahrenstechnik series. AVT.CVT - chemical
                      process engineering},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2026},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2026},
      abstract     = {Gas diffusion electrodes (GDEs) are critical components in
                      electrochemical energy conversion systems such as fuel
                      cells, water electrolyzers, and CO2 reduction cells. Here,
                      precise control of gas-liquid distribution within porous
                      structures determines de-vice performance. This thesis
                      advances the fundamental understanding and predictive
                      modeling of two-phase flow phenomena in GDEs through the
                      development of novel computational frameworks spanning from
                      detailed morphological simulations to novel, generalizable
                      machine learning approaches. The research first addresses
                      the challenge of mixed wettability in gas diffusion layers
                      by developing algorithms that account for multiple materials
                      with distinct contact angles. Applied to both stochastically
                      reconstructed and μ-CT scanned structures, the models
                      reveal that polytetrafluoroethylene (PTFE) surface coverage,
                      rather than weight percentage, governs wetting behavior. A
                      critical finding emerges: minimum surface coverage of $50\%$
                      is required to achieve meaningful improvements in
                      break-through pressure and gas diffusion, while coverage
                      exceeding $80\%$ yields diminishing returns. The wetting
                      model is then extended to catalyst layers through by
                      combining full morphology approaches with stochastic
                      invasion percolation. This framework captures time-dependent
                      phenomena including fluid trapping and electrowetting
                      effects. Three-stage simulations mimicking operational
                      conditions demonstrate that electrowetting at typical
                      operating potentials (1 V) increases saturation to $80\%$
                      regardless of initial ionomer coverage, completely negating
                      hydrophobic design strategies. Surprisingly, gas
                      backpressure up to 50 kPa produces minimal saturation
                      changes $(5-20\%),$ suggesting that experimental reports of
                      pressure-based water management likely result from
                      macroscopic defects rather than intrinsic pore drainage. To
                      address computational limitations and enable rapid
                      prototyping, the thesis explores foundation models for
                      physics simulation through the development of the General
                      Physics Transformer (GPhyT). Trained on 1.8 TB of diverse
                      simulation data, this transformer-based architecture
                      demonstrates emergent in-context learning capabilities. Most
                      significantly, the model exhibits zero-shot generalization
                      to unseen boundary conditions and produces physically
                      plausible predictions for entirely novel phenomena,
                      establishing the feasibility of "train once, deploy
                      anywhere" paradigms for computational physics. With more
                      GDE-based data, such a foundation model could accelerate
                      prototyping in GDE design. This work establishes that
                      effective GDE optimization requires consideration of
                      dynamic, potential-dependent wetting phenomena rather than
                      relying solely on static material properties, fundamentally
                      shifting the paradigm for electrode design in
                      next-generation electrochemical systems.},
      cin          = {416110},
      ddc          = {620},
      cid          = {$I:(DE-82)416110_20140620$},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2026-01948},
      url          = {https://publications.rwth-aachen.de/record/1028896},
}