Research Papers

An Ensemble Monte Carlo Simulation Study of Water Distribution in Porous Gas Diffusion Layers for Proton Exchange Membrane Fuel Cells

[+] Author and Article Information
Luigino Capone, Philip Marmet, Lorenz Holzer, Jaka Dujc

Institute of Computational Physics,
Zurich University of Applied Sciences,
Winterthur 8400, Switzerland

Jürgen O. Schumacher

Institute of Computational Physics,
Zurich University of Applied Sciences,
Winterthur 8400, Switzerland
e-mail: juergen.schumacher@zhaw.ch

Adrien Lamibrac, Felix N. Büchi

Electrochemistry Laboratory,
Paul Scherrer Institut,
Villigen PSI 5232, Switzerland

Jürgen Becker

Math2Market GmbH,
Kaiserslautern 67655, Germany

1Corresponding author.

Manuscript received July 28, 2016; final manuscript received September 5, 2017; published online April 9, 2018. Assoc. Editor: Jan Van Herle.

J. Electrochem. En. Conv. Stor. 15(3), 031005 (Apr 09, 2018) (10 pages) Paper No: JEECS-16-1102; doi: 10.1115/1.4038627 History: Received July 28, 2016; Revised September 05, 2017

Water management in proton-exchange membrane fuel cells (PEFCs) has a large impact on the performance of the device, as liquid water affects the transport properties of the gas diffusion layer (GDL). In this study, we develop an ensemble-based model of the liquid water distribution inside the GDL. Based on a water injection experiment, the wet structure of the porous medium is inspected via X-ray tomographic microscopy and, after an image segmentation process, a voxel-based meshing of the fiber, air, and water domains is obtained. Starting from the obtained dry fiber structure, a Metropolis-Hastings Monte Carlo algorithm is used to obtain the equilibrium distribution of liquid water that minimizes the surface free energy of the ensemble. The different water distributions from the Monte Carlo (MC) simulation and water injection experiment are identified as solution for different physical mechanisms both of which are present in a running fuel cell. The wet structure is then used to calculate saturation-dependent effective transport properties using the software geodict. Thereby, a strong influence of the saturation gradient on the macrohomogeneous transport properties is found.

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Fig. 1

Contact angle and surface tensions at the three-phase interfaces

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Fig. 2

Sketches of different surface interactions

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Fig. 3

Energy minimization as a function of MC iterations. Different initial randomized distributions (seeds) are compared.

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Fig. 4

Capillary pressure simulated via MC (different seeds) and Young–Laplace (blue)

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Fig. 5

Experimental setup scheme: pressurized water is injected in the GDL and X-ray tomographic data are acquired

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Fig. 6

Simulated water invasion from the bottom, size 1126 μm × 1126 μm × 70 μm. Fibers (brown) and water (light blue).

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Fig. 7

Slice-averaged water fraction (experimental in red, simulated in blue) and porosity (black) along the through-plane direction

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Fig. 8

MC water distribution for symmetric top and bottom boundary conditions, plotted for the central slice. Fibers (black) and water (light cyan).

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Fig. 9

Differential picture of experiment and MC simulation. Fibers (black), water for both cases (blue), water only in the experiment (red), and water only in the MC simulation (green). (a) Top slice (hydrophobic membrane side), (b) central slice, and (c) bottom slice (water injection side).

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Fig. 10

Slice-averaged water fraction and porosity along the z-direction for different average water saturations for the experiment and the MC simulation: (a) Water injection experiment and (b) MC simulation with symmetric through-plane boundary conditions

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Fig. 11

M-factor (gaseous phase) for MC and experiment for different average water saturations

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Fig. 12

Liquid water permeability in through-plane direction for MC and experiment for different average water saturations



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