Evaluation of gas permeability in porous separators for polymer electrolyte fuel cells: Computational fluid dynamics simulation based on micro-x-ray computed tomography images

Pore structures and gas transport properties in porous separators for polymer electrolyte fuel cells are evaluated both experimentally and through simulations. In the experiments, the gas permeabilities of two porous samples, a conventional sample and one with low electrical resistivity, are measured by a capillary flow porometer, and the pore size distributions are evaluated with mercury porosimetry. Local pore structures are directly observed with micro-x-ray computed tomography (CT). In the simulations, the effective diffusion coefficients of oxygen and the air permeability in porous samples are calculated using random walk Monte Carlo simulations and computational fluid dynamics (CFD) simulations, respectively, based on the x-ray CT images. The calculated porosities and air permeabilities of the porous samples are in good agreement with the experimental values. The simulation results also show that the in-plane permeability is twice the through-plane permeability in the conventional sample, whereas it is slightly higher in the low-resistivity sample. The results of this study show that CFD simulation based on micro-x-ray CT images makes it possible to evaluate anisotropic gas permeabilities in anisotropic porous media.

Figure 1

Schematics of (a) an internally humidified PEFC and (b) a PEFC with porous separators. The inset shows a scanning electron microscope image of a porous separator.

Figure 2

Illustration of a porous sample on the stage and the cylindrical image scanned by x-ray CT.

Figure 3

(a) 3D image of a porous sample and its cross-sectional images in (b) $x\text{−}y$, (c) $x\text{−}z$, and (d) $y\text{−}z$ planes (sample A).

Figure 4

Images of the entire cylindrical scanned region with 1 mm in diameter and 1 mm in height: (a) solid; (b) void; (c) open pore; (d) effectively closed pores (sample A).

Figure 5

Cubic analysis region of $200\times 200\times 200$ voxels extracted from a cylindrical scanned region with 1 mm in diameter and 1 mm in height.

Figure 6

Images of the cubic analysis region with the side of 200 $\mu \mathrm{m}$: (a) solid; (b) void; (c) open pore; (d) effectively closed pores (sample A).

Figure 7

STL data for cubic analysis regions with sides of (a) 200 $\mu \mathrm{m}$ and (b) 100 $\mu \mathrm{m}$, and volume meshes for cubic analysis regions with sides of (c) 200 $\mu \mathrm{m}$ and (d) 100 $\mu \mathrm{m}$ (sample A).

Figure 8

(a) Air flow rate and (b) air permeability of samples A and B measured by a capillary flow porometer.

Figure 9

Pore size distributions of samples A and B measured by mercury porosimetry: probability density functions (PDFs) of pore diameter $d_p$.

Figure 10

Porosity distributions of (a) sample A and (b) sample B.

Figure 11

Chord length distributions in random directions of samples A and B.

Figure 12

Measured pore size distributions and calculated chord length distributions in Cartesian directions of (a) sample A and (b) sample B.

Figure 13

Mean-square displacements (MSDs), and their $x$, $y$, and $z$ components, of oxygen molecules in (a) sample A and (b) sample B. The insets show the same graphs on a linear scale.

Figure 14

(a) Streamlines, (b) pressure distribution, and (c) velocity magnitude distribution for the flow in the $y$ direction at $\mathrm{\Delta }P=40$ kPa (sample A).

Figure 15

Comparison between calculated and experimental results for air permeabilities in the $x$, $y$, and $z$ directions: (a) effect of analysis region size; (b) effect of sample type for a porous cube of side 100 $\mu \mathrm{m}$.

Figure 16

Binarized cross sectional images of sample A with five different threshold options (moments, default, mean, percentile, and Otsu).

Figure 17

Brightness histogram and calculated porosity for each threshold option of sample A.

Figure 18

Mean-square displacements (MSDs), and their $x$, $y$, and $z$ components, of oxygen molecules in sample A at $\lambda =68$ nm ($\mathrm{Kn}=2.76\times {10}^{-2}$).

Figure 19

2D FFT images (top) and cross-sectional images (bottom) of a porous sample in (a) $x\text{−}y$, (b) $x\text{−}z$, and (c) $y\text{−}z$ planes (sample A).